{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "711bc155-8d7d-46e6-9ca0-b4b859515066",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fix python path if working locally\n",
    "from utils import fix_pythonpath_if_working_locally\n",
    "\n",
    "fix_pythonpath_if_working_locally()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d3da8657-2bff-43e1-a96f-db149fa9f409",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a46ecf12-0d29-459f-bd2a-90ae4c745df6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from darts import TimeSeries\n",
    "\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import logging\n",
    "\n",
    "logging.disable(logging.CRITICAL)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f32480c-db7d-4e9f-8ebc-bccf004d817e",
   "metadata": {},
   "source": [
    "# Static Covariates\n",
    "\n",
    "Static covariates are characteristics of a time series / constants which do not change over time. When dealing with multiple time series, static covariates can help specific models improve forecasts. Darts' models will only consider static covariates embedded in the target series (the series for which we want to predict future values) and not past and/or future covariates (external data).\n",
    "\n",
    "In this tutorial we look at:\n",
    "\n",
    "1. how to define static covariates (numeric and/or categorical)\n",
    "2. how to add static covariates to an existing **target** series\n",
    "3. how to add static covariates at TimeSeries creation\n",
    "4. how to use TimeSeries.from_group_dataframe() for automatic extraction of TimeSeries with embedded static covariates\n",
    "5. how to scale/transform/encode static covariates embedded in your series\n",
    "6. how to use static covariates with Darts' models\n",
    "\n",
    "We start by generating a multivariate time series with three components `[\"comp1\", \"comp2\", \"comp3\"]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "13e632a4-ff6a-4d58-be65-b6bebc37ca2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "series = TimeSeries.from_times_and_values(\n",
    "    times=pd.date_range(start=\"2020-01-01\", periods=10, freq=\"h\"),\n",
    "    values=np.random.random((10, 3)),\n",
    "    columns=[\"comp1\", \"comp2\", \"comp3\"],\n",
    ")\n",
    "series.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd068f2c-bc9c-45c7-b757-a7feae6449d5",
   "metadata": {},
   "source": [
    "### 1. Defining static covariates\n",
    "Define your static covariates as a pd.DataFrame where the columns represent the static variables and rows stand for the components of the uni/multivariate `TimeSeries` they will be added to.\n",
    "\n",
    "- The number of rows must either be 1 or equal to the number of components from `series`.\n",
    "- Using a single row static covariate DataFrame with a multivariate (multi component) `series`, the static covariates will be mapped globally to all components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "90431347-dca7-4dee-b7e4-ef4911da605c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   cont cat\n",
      "0     0   a\n",
      "   cont cat\n",
      "0     0   a\n",
      "1     2   c\n",
      "2     1   b\n"
     ]
    }
   ],
   "source": [
    "# arbitrary continuous and categorical static covariates (single row)\n",
    "static_covs_single = pd.DataFrame(data={\"cont\": [0], \"cat\": [\"a\"]})\n",
    "print(static_covs_single)\n",
    "\n",
    "# multivariate static covariates (multiple components). note that the number of rows matches the number of components of `series`\n",
    "static_covs_multi = pd.DataFrame(data={\"cont\": [0, 2, 1], \"cat\": [\"a\", \"c\", \"b\"]})\n",
    "print(static_covs_multi)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "127de718-dda2-4423-a02a-b354ef43ba08",
   "metadata": {},
   "source": [
    "### 2. Add static covariates to an existing TimeSeries\n",
    "Create a new series from an existing TimeSeries with added static covariates using method `with_static_covariates()` (see docs [here](https://unit8co.github.io/darts/generated_api/darts.timeseries.html#darts.timeseries.TimeSeries.with_static_covariates))\n",
    "\n",
    "- Single row static covarites with multivariate `series` creates \"global_components\" which are mapped to all components\n",
    "- Multi row static covarites with multivariate `series` will be mapped to the component names of `series` (see static covariate index/row names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "80e40a2e-508d-4494-a066-fa70c1cd520f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Single row static covarites with multivariate `series`\n",
      "static_covariates  cont cat\n",
      "global_components   0.0   a\n",
      "\n",
      "Multi row static covarites with multivariate `series`\n",
      "static_covariates  cont cat\n",
      "component                  \n",
      "comp1               0.0   a\n",
      "comp2               2.0   c\n",
      "comp3               1.0   b\n"
     ]
    }
   ],
   "source": [
    "assert series.static_covariates is None\n",
    "\n",
    "series_single = series.with_static_covariates(static_covs_single)\n",
    "print(\"Single row static covarites with multivariate `series`\")\n",
    "print(series_single.static_covariates)\n",
    "\n",
    "series_multi = series.with_static_covariates(static_covs_multi)\n",
    "print(\"\\nMulti row static covarites with multivariate `series`\")\n",
    "print(series_multi.static_covariates)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "638f47fb-282c-45d5-8708-42af483912a3",
   "metadata": {},
   "source": [
    "### 3. Adding static covariates at TimeSeries construction\n",
    "Static covariates can also directly be added when creating a time series with parameter `static_covariates` in most of `TimeSeries.from_*()` methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2106e10b-8a32-4e48-9d3c-2c77bfab4473",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "static_covariates  cont cat\n",
      "component                  \n",
      "comp1               0.0   a\n",
      "comp2               2.0   c\n",
      "comp3               1.0   b\n"
     ]
    }
   ],
   "source": [
    "# add arbitrary continuous and categorical static covariates\n",
    "series = TimeSeries.from_values(\n",
    "    values=np.random.random((10, 3)),\n",
    "    columns=[\"comp1\", \"comp2\", \"comp3\"],\n",
    "    static_covariates=static_covs_multi,\n",
    ")\n",
    "print(series.static_covariates)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9caa4638-35d0-4397-98eb-672b18d94115",
   "metadata": {},
   "source": [
    "### Using static covariates with multiple TimeSeries\n",
    "Static covariates are only really useful if we use them across multiple TimeSeries. By convention, the static covariates layout (pd.DataFrame columns, index) has to be the same for all series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b6af377e-e0b8-4e36-9df7-d625789b95ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valid static covariates for multiple series\n",
      "static_covariates       ID  var1\n",
      "global_components  SERIES1   0.5\n",
      "static_covariates       ID  var1\n",
      "global_components  SERIES2  0.75\n"
     ]
    }
   ],
   "source": [
    "first_series = series.with_static_covariates(\n",
    "    pd.DataFrame(data={\"ID\": [\"SERIES1\"], \"var1\": [0.5]})\n",
    ")\n",
    "second_series = series.with_static_covariates(\n",
    "    pd.DataFrame(data={\"ID\": [\"SERIES2\"], \"var1\": [0.75]})\n",
    ")\n",
    "\n",
    "print(\"Valid static covariates for multiple series\")\n",
    "print(first_series.static_covariates)\n",
    "print(second_series.static_covariates)\n",
    "\n",
    "series_multi = [first_series, second_series]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18b91481-969b-449c-9f90-3ee749a93f43",
   "metadata": {},
   "source": [
    "### 4. Extract a list of time series by groups from a DataFrame using `from_group_dataframe()`\n",
    "If your DataFrame contains multiple time series which are stacked vertically, you can use `TimeSeries.from_group_dataframe()` (see the docs [here](https://unit8co.github.io/darts/generated_api/darts.timeseries.html#darts.timeseries.TimeSeries.from_group_dataframe)) to extract them as a list of TimeSeries instances. This requires a column or list of columns for which the DataFrame should grouped by (parameter `group_cols`).\n",
    "`group_cols` will automatically be added as static covariates to the individual series. Additional columns can be used as static covariates with parameter `static_cols`. \n",
    "\n",
    "In the example below, we generate a DataFrame which contains data of two distinct time series (overlapping/repeating dates) \"SERIES1\" and \"SERIES2\" and extract the TimeSeries with `from_group_dataframe()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "643fe7fd-0442-42ef-9109-0b58b720edd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input DataFrame\n",
      "        dates     comp1     comp2     comp3       ID  var1\n",
      "0  2020-01-01  0.158970  0.820993  0.976761  SERIES1  0.50\n",
      "1  2020-01-02  0.110375  0.097101  0.604846  SERIES1  0.50\n",
      "2  2020-01-03  0.656330  0.837945  0.739264  SERIES1  0.50\n",
      "3  2020-01-01  0.138183  0.096098  0.039188  SERIES2  0.75\n",
      "4  2020-01-02  0.196582  0.976459  0.282807  SERIES2  0.75\n",
      "5  2020-01-03  0.368725  0.468651  0.120197  SERIES2  0.75\n",
      "\n",
      "2 series were extracted from the input DataFrame\n",
      "Static covariates of series 0\n",
      "static_covariates       ID  var1\n",
      "global_components  SERIES1   0.5\n",
      "Static covariates of series 1\n",
      "static_covariates       ID  var1\n",
      "global_components  SERIES2  0.75\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate an DataFrame example\n",
    "df = pd.DataFrame(\n",
    "    data={\n",
    "        \"dates\": [\n",
    "            \"2020-01-01\",\n",
    "            \"2020-01-02\",\n",
    "            \"2020-01-03\",\n",
    "            \"2020-01-01\",\n",
    "            \"2020-01-02\",\n",
    "            \"2020-01-03\",\n",
    "        ],\n",
    "        \"comp1\": np.random.random((6,)),\n",
    "        \"comp2\": np.random.random((6,)),\n",
    "        \"comp3\": np.random.random((6,)),\n",
    "        \"ID\": [\"SERIES1\", \"SERIES1\", \"SERIES1\", \"SERIES2\", \"SERIES2\", \"SERIES2\"],\n",
    "        \"var1\": [0.5, 0.5, 0.5, 0.75, 0.75, 0.75],\n",
    "    }\n",
    ")\n",
    "print(\"Input DataFrame\")\n",
    "print(df)\n",
    "\n",
    "series_multi = TimeSeries.from_group_dataframe(\n",
    "    df,\n",
    "    time_col=\"dates\",\n",
    "    group_cols=\"ID\",  # individual time series are extracted by grouping `df` by `group_cols`\n",
    "    static_cols=[\n",
    "        \"var1\"\n",
    "    ],  # also extract these additional columns as static covariates (without grouping)\n",
    "    value_cols=[\n",
    "        \"comp1\",\n",
    "        \"comp2\",\n",
    "        \"comp3\",\n",
    "    ],  # optionally, specify the time varying columns\n",
    ")\n",
    "\n",
    "print(f\"\\n{len(series_multi)} series were extracted from the input DataFrame\")\n",
    "for i, ts in enumerate(series_multi):\n",
    "    print(f\"Static covariates of series {i}\")\n",
    "    print(ts.static_covariates)\n",
    "    ts[\"comp1\"].plot(label=f\"comp1_series_{i}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71c466ae-b136-4307-a9b5-550554ffc467",
   "metadata": {},
   "source": [
    "### 5. Scaling/Encoding/Transforming static covariate data\n",
    "There might be the need to scale numeric static covariates or encode categorical static covariates as not all models can handle non numeric static covariates. \n",
    "\n",
    "Use `StaticCovariatesTransformer` (see the docs [here](https://unit8co.github.io/darts/generated_api/darts.dataprocessing.transformers.static_covariates_transformer.html#staticcovariatestransformer)) to scale/transform static covariates. By default it uses a `MinMaxScaler` to scale numeric data, and a `OrdinalEncoder` to encode categorical data.\n",
    "Both the numeric and categorical transformers will be fit globally on static covariate data of all time series passed to `StaticCovariatesTransformer.fit()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "32455f97-f147-4b02-9838-11632488b25f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original series 0\n",
      "static_covariates       ID  var1\n",
      "global_components  SERIES1   0.5\n",
      "Transformed series 0\n",
      "static_covariates   ID  var1\n",
      "global_components  0.0   0.0\n",
      "\n",
      "Original series 1\n",
      "static_covariates       ID  var1\n",
      "global_components  SERIES2  0.75\n",
      "Transformed series 1\n",
      "static_covariates   ID  var1\n",
      "global_components  1.0   1.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from darts.dataprocessing.transformers import StaticCovariatesTransformer\n",
    "\n",
    "transformer = StaticCovariatesTransformer()\n",
    "series_transformed = transformer.fit_transform(series_multi)\n",
    "\n",
    "for i, (ts, ts_scaled) in enumerate(zip(series_multi, series_transformed)):\n",
    "    print(f\"Original series {i}\")\n",
    "    print(ts.static_covariates)\n",
    "    print(f\"Transformed series {i}\")\n",
    "    print(ts_scaled.static_covariates)\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6db96f9-4b1f-4f7f-9897-144b8e8b270d",
   "metadata": {},
   "source": [
    "### 6. Forecasting example with `TFTModel` and static covariates\n",
    "Now let's find out if adding static covariates to a forecasting problem can improve predictive accuracy.\n",
    "We'll use `TFTModel` which supports numeric and categorical static covariates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a0e90425-1196-4a84-8f5e-b5a1597bcda5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from pytorch_lightning.callbacks import TQDMProgressBar\n",
    "\n",
    "from darts import TimeSeries\n",
    "from darts.models import TFTModel\n",
    "from darts.utils import timeseries_generation as tg\n",
    "from darts.dataprocessing.transformers import StaticCovariatesTransformer\n",
    "from darts.metrics import rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58c2fd83-6a2d-4b44-a88a-a5579695011e",
   "metadata": {},
   "source": [
    "#### 6.1 Experiment setup\n",
    "For our experiment, we generate two time series: a fully sine wave series (label = smooth) and sine wave series with some irregularities every other period (label = irregular, see the ramps at periods 2 and 4)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1f8af919-e0cb-4d4c-9577-04954421e187",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "period = 20\n",
    "sine_series = tg.sine_timeseries(\n",
    "    length=4 * period, value_frequency=1 / period, column_name=\"smooth\", freq=\"h\"\n",
    ")\n",
    "\n",
    "sine_vals = sine_series.values()\n",
    "linear_vals = np.expand_dims(np.linspace(1, -1, num=19), -1)\n",
    "\n",
    "sine_vals[21:40] = linear_vals\n",
    "sine_vals[61:80] = linear_vals\n",
    "irregular_series = TimeSeries.from_times_and_values(\n",
    "    values=sine_vals, times=sine_series.time_index, columns=[\"irregular\"]\n",
    ")\n",
    "sine_series.plot()\n",
    "irregular_series.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "783504a3-65f3-40bc-8a1f-ed5a090b98e7",
   "metadata": {},
   "source": [
    "We will use three different setups for training and evaluation:\n",
    "\n",
    "1. fit/predict without static covariates\n",
    "2. fit/predict with binary (numeric) static covariates\n",
    "3. fit/predict with categorical static covariates\n",
    "\n",
    "For each setup we'll train the model on both series and then only use only the 3rd period (sine wave for both series) to predict the 4th period (sine for \"smooth\" and ramp for \"irregular\").\n",
    "\n",
    "What we hope for is that the model without static covariates performs worse than the other ones. The non-static model should not be able to recognize whether the underlying series used in `predict()` is the *smooth* or the *irregular* series as it only gets a sine wave curve as input (3rd period). This should result in a forecast somewhere inbetween the smooth and irregular series (learned by minimizing the global loss during training).\n",
    "\n",
    "Now this is where static covariates can really help. For example, we can embed data about the curve type into the **target** series through static covariates. With this information, we'd expect the models to generate improved forecasts.\n",
    "\n",
    "First we create some helper functions to apply the same experiment conditions to all models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "76279b10-1c94-46c7-9b91-6b59398a6c8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_case(model, train_series, predict_series):\n",
    "    \"\"\"helper function which performs model training, prediction and plotting\"\"\"\n",
    "    model.fit(train_series)\n",
    "    preds = model.predict(\n",
    "        n=int(period / 2), num_samples=250, series=predict_series, verbose=False\n",
    "    )\n",
    "    for ts, ps in zip(train_series, preds):\n",
    "        ts.plot()\n",
    "        ps.plot()\n",
    "        plt.show()\n",
    "    return preds\n",
    "\n",
    "\n",
    "def get_model_params():\n",
    "    \"\"\"helper function that generates model parameters with a new Progress Bar object\"\"\"\n",
    "    return {\n",
    "        \"input_chunk_length\": int(period / 2),\n",
    "        \"output_chunk_length\": int(period / 2),\n",
    "        \"add_encoders\": {\n",
    "            \"datetime_attribute\": {\"future\": [\"hour\"]}\n",
    "        },  # TFTModel requires future input, with this we won't have to supply any future_covariates\n",
    "        \"random_state\": 42,\n",
    "        \"n_epochs\": 150,\n",
    "        \"pl_trainer_kwargs\": {\n",
    "            \"callbacks\": [TQDMProgressBar(refresh_rate=4)],\n",
    "        },\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99faf9bc-305a-4897-8f10-5f8ac3777601",
   "metadata": {},
   "source": [
    "### 6.2 Forecasting without static covariates\n",
    "Let's train the first model without any static covariates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7a8411c3-b02a-4b19-97bb-46a253f43338",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6bdf2f0868df4253b621aa3236eee2ab",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d52eb2242b3c4f25bfc82fe27f631419",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 4it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ldOKh2datW2ecy+nI62xuxiLMYh2fFDQNbgTgPVvGFn2+dwfl3R9y8NoHpxsO4/XBlDc5wqjzkTQOfmBggJaWFtLT01m3bt2Sz+PEnlUkJicnOX78OC6Xi82bNy/5PMmkWSAQ4PDhw4Dswc9mfGJuB7//MARcWTD0KtvrVi/6fFvWQl4WtPRAS7cIOfjGU4dkVckgSePg9Ztu8+bNUU1wmo3eG3XSjReJ48eP4/P5qK2tnXOpucXiRGcViebmZoaHhykrK2P58uVLPo+u2ZEjRwgEAvEyz1TGI8xi/e1LXgA8/f9JRdWaRZ/Pk+Ji5ybt5/2HYdWaTbjdblqbTzA1NSEdPEnk4OMRF4XpDIeGhgZGR50d6IuXZitXriQ3N5eenh7HLwASL82WLVtGRUUFo6OjjlkAZHzy/Do0XX2CI82p4B9jbeHJqDO19DDNvkOQkZlFVc06An4/HS3vMDYpQzTSwUeJUzMc5iJemrnd7lC6pNN78fHSLPwcTtFsYur8EM1TeqHRs79k3brF99516tZDZjrUt2sPCz1M09EsM2lAOvgl4bQbLxJSs+iRms2NEOI8Bx8ICJ5+M/hLz0Mh5xwNaakutl+o/bz/0HQmTevpQ7JsMEni4H0+H0ePHgVY0szC2TjpxouEECKuzipZxi6kg58bnx8QM9difbseevrB4++AwRdZU7s0zUJhmsPTmTQtjTJVEpLEwdfX1zMxMcGKFSsoKCiI+XxOuvEi0dnZSV9fH4WFhVRVVcV8vmTQbGhoiKamJtLT01m/fn3M53OSZl4f5xWi0fPX/Z0P4XLB6tqlZWpdeiGkpcKxJigsvxiA0/WHGJ8U+ObJvU8GksLBx7NXFX6ew4cPOybDYTbhmi1mBaeF2LRJy3A4ceIEExMTMZ/PiuiZWhs3bowpU0untraWjIwMWltbGRgYiPl8ZuL1nV9orCU43i4G9lNRtYbMrJwlnTsz3cUlF2g/H+8sI6+gmJHhAfrOtDGe5FUlk8LBx2PiSTjLli2jvLyckZERTp8+HZdzWo14a5aVlUVtbS0+n4933nknLue0GvHWzOPxsGmTlgdod828vvPr0LTqCVXjJ5cUfw9Hn9X65glCoZ6WxkNJXzY4KRx8vHvw4edywuvzXEjNoieRmjnBwYczMiY4NwwprimYbAsNji6V9TXa9+au6YHWltOHkz4XPqkcvD7QFw+cPmiYCM2SxcEn4jo7fvx43M5pBt7gIKtO6xnte5q/DRCsXbc1pvNXLdMydDr7oGatAkBb0yEGhmUM3tGE1zNftWpV3M7rZGcVXs/8wgsvjNt5nfxQ9Pv9cc3U0nFKD35iUhA+h6kt6OB9Q9pcklhDNKkeF1XLtJ8zizUH39J4iHNJnirpeAcfaz3zSDjZwcdazzwSTq6nX19fz/j4ODU1NRQWFsbtvPrD4tSpU7aupz8+q9CYHn/3Dh4mJ7eA0rLqmNtYWa59n3SvwuNJpbujka6e5PbwSePg4xkXhekMh5aWFttnOMwmUZo5uZ5+ojRzSj39sVllCvQevD7AGo9MrVVBB9/W66FmlTbb/OTJI3iTOFVSOvglEp7hoKfHOYVEaebkevqJ0iz8nHbWbHzWLFY9Bs/YyZgHWHVWlmnftYFWTbPW04cYc2ZW7qJwvIM/duwYQEzlbiOhvz7rsVenIDWLHqnZ/ExMTleS9PsFnb3BDeOnWL02PprpPfimLlgTnDTV3nQsqTNpHO3ghRCcOnUKIC4zC2ej15Wvr6+P+7nNRGoWPVKz+Qlf7KO7X8uq8QR6IDBK9Yr4aFZRAqkeODMAJRVackB3Zz0j4zJE40h6enoYGRmhsLCQ4uLiuJ+/trYWsPeNN5vR0VE6OztJS0ujpqYm7ud3omZ+vz804W3t2rVxP78TNPP6CC3Fp4dnxKiW+llVUxuXNlLcLmr0EvyZGwDo6Wigfzgup7cljnbw+g0RywpO8+GEntVsGhoaAFizZg0ps4t3xwEnatba2srU1BSVlZUxLYwSiXAHb8fso9kmtwUzaPzDx8jKzqWweOkLo8xmVTAOPxKowu1209vTQk9f8sZoHO3g9ddm/QaJN2vWaPWrT58+besUtnASrVlVVRUZGRl0d3czNDSUkDaMJtGaFRUVUVhYyNjYGJ2dnQlpI5EIMbMOzfQA6wkqq2vjkkGjo6dKtvd6WF6+gkAgQH39aVs+GONBzA5eUZRvKoqyX1GUnyiKkhr2+S5FUdoURXlRUZS9sbazFPReYqJuvMzMTKqrq/H5fDQ3NyekDaNJtGZutzv0YNTfFuxOojUDQpP07PjmM9u1TqdInopbeEYnfKC1slo7d2d7fdLWpInJwSuKsgWoVFX1CuAE8IFZuzyqquouVVWviqWdpZLoEE34ue14482F1Cx69B58IjVbvVpbjNqOmkUK0TB2gqoV8XXwoVTJbqgOnru7vSFpUyVj7cHvBJ4J/vwUcNms7X8W7N1/KsZ2lkSiX53Dz623ZXekZtFjZA/ejpoJQShGMzwmODcCbiZgqoOq6vhqVlYEGWnQNwTFFdo8la6OU9LBL5FCQA+kDgJFYdtUYD1wFbBHUZS6GNuKikAgEAoBGOGs7NizmgsjnJXTNDPioeiUHrwenkn1NgOCqhUz33oGRwXd/YLuPu2rK/g1NrG4GLrb7WJFcMzWk6tNdurpaGBwNDlj8LGuSjAA5AV/zgf69Q2qqoaKQCiK8htgC/BW+MGKotwK3Apwxx13sHv37iUb4vV66ejoCP3e0dHBxMQEy5YtY3h4mOHhxORK6emXR44cmdH+Ypltt5kMDg7S29tLZmYmQoh57YrFbr1Wy9GjRw3/2+Ot99TUFM3NzbhcLjIyMhL291RXa7Va3nnnHctcL4tFCC+bKrvwpMCrA5lAAb5hrcjYuzbkUJAzPXBcnAG5WeB2g8uldfy9fhifAH9g5ueRxmY3VOdzsi2L1Cyt+tjZruOkBrqIVjYr3ZvzUVlZGXFbrA7+FeAzwCPAtcDL+gZFUfJUVdV795cD984+WFXV+4H7g7/G9Ijt6OiY8YeeOHEC0CaezCdArGzfvh3QUuWW0s5su81Ez9Cora0NOZRIxGL3zp07AWhpaTH8b4+33idPniQQCLBy5cpQLzsRjI5qC4y2tLRQVlaWkBTWRFHf2EH9mXIKc10cbdNuc//wMfIKipnwbKQ72BUcmxCkpMC1G84PLAghOHMOGjsFI+Na8bLJKS2/vnLZTE9ftkxro3VgJR5PKj093RxuzufPr45uxSgr3ZtLJaYQjaqqB4EeRVH2AxuBxxVFuS+4+SZFUd5QFOUVoENV1X2xmRodRgwWghYbTUlJoaWlhclJe+fbGqVZWVkZOTk59Pf309fXl9C2Eo0RA6wA2dnZlJeXMzU1RVtbW0LbijcBMT2LdXoVp1Pnxd+Hx6cHSWfjcrlYXuRi5yY311zi5k8ud3PTH7kpzue88I1+jpYeN+VV2kP3dFNDUq7PGvPCkaqq3jXro9uCnz8APBDr+ZeKEXFRgLS0NFauXEljYyONjY1xrZ9uNEZp5nK5qK2t5e2336a+vj4hs4yNwogxC53a2lq6uro4deoUK1euTHh78SIQmHbwbeE58DUzi4wJAaWF0eXEr6+G196BrIzpz8JTJS+srqWt+STd7acYn9pCbuxL5doKx050MvrGC2/TrkjNoseohyLYN71UCK1UsN8v6Dgb/HC8PpTGCODzC1JTID/KicDlxdoDIRCY7p0vK4DsDBgchZKqiwHoStJUScc6eKNencPbsGMKWzhSs+gxKqwF9k0vDQitkmRXH/j8kE4vBMZCE5EAhkahZrmWBRMNGekuqktheGz6M5fLxYpgmCa9UEve6+44xegiM3GchCMdvM/nCxV/0mdNJhIn9EaFELIHvwSM1MyOPXifTyDQHLdeosA1odlfHZYiOemFqmVLK1mwpsJ1Xu9cD9P40rVKlT0dDZxLwqJjjnTwLS0t+Hw+qqqqyMrKSnh7TnBWZ8+eZXBwkLy8PJYtW5bw9pyg2djYGG1tbXg8HkNi4nbUzOuf/rk9WAN+akBbIKeyWqu8KYTABRTlsSSWFUBqqhbm0dFz4Uf9FQB0d9RLB+8UjAw1hLdjt1fncMI1i2fxp0iEa2bXQlCNjY2ANgnJ40n86N2aNWtwuVw0NTXh9XoT3l488IbV4OsPJk0HxpspKikjKzsXgNEJWFYI6WlLu+48HhdrK5jhwPUFuPtGs0lLz2Dg3Bk6ugdte60tFUc6eCNfmwFqampIS0ujs7MzlK9sN4zWrLi4mMLCQkZGRujp6Vn4AAti5AArQEZGBjU1Nfj9fpqamgxpM1Z8/ulKkn2DwR+meqiqme58jYxFTo9cLDVlrhkPk8qgg+846wqlY7a31DOZZEXHpIOPAykpKaFJLnatkGi0ZuFt2SnkEI7UbGHCO8x9+rTHqS6qamYujFKSH9tbY2GuNgN2YkprsLwY3C44cw7Kq4OrO3XUMy4dvP0xOkQT3pZdwzRSs+iRmkVHaGWlqe5QD37KJ8hIh7wY10lxuVysr4bB4KzYVI+LsmLtAVNQsQ2Arg7Zg3cEsmcVPVKz6JGaRYceg8fbFaoDPzyqDYjGY9xneZFrRr0TPQ6fmqctwN3dYT/NYsVxDn5ycpKWlhbcbndCa4PMxs49K6NTJHXsrBkYmwOvY9dc+Cmf0HLVhRe8fSEH7/VBWVF8BvVzMrV4vz7pSY/Di3TND3S3Swdve06fPh0q/pSWlmZYu3buWXV2djI2NkZJSUmo0qMR2FmzoaEhenp6yMjIoKqqyrB27ZgLD2G996keXC6oqJqen5ITp0zmlBQXRXlaTj1M9+DHhDaC291hzzVtY8FxDt6Mnmh4e3a78cB8zRoaGggEAoa2HSu6ZmvWrMHtNu42WrlyJSkpKbS1tTE+Pm5Yu7HSHzbAWrq8mvQMrSQ1LshKj187ywsJTXrSHXzvcCZZ2bmMjgzQ32/v4nbR4jgHb8bAF0BFRQVZWVn09vYyMDBgaNuxYpZmeXl5LF++nImJCdrb2w1tO1bM0iw1NZXVq1cjhAjl4duB8B68vsjHlBfysrSed7wozp9OlwylSvYSGtQ93Wiv0FasOM7Bm9UbdbvdrF27doYNdsEszcLblJotHjtqFp4iWRlMkZyYWvrs1UjkZhFKvC8rhBQ39A5AefVGAJpO20ezeOA4B29kDZrZ6G3qNtgFqVn0SM2iY4aDD8bfx6egKDe+7WRnTA+0pqS4qCjRPs8p1YqOtTTbR7N44DgHr8/wMzKDRkdv0y6zDHWkZtEjNYuO6RTJHsorpzXLy45vWYyUFBfFedrbAUzH4VNytMlOra3NcW3P6jjKwft8PlpaWgBMWRBh1apVgL16VuGxXN1+I7GjZjBtr9RscYQPspZXBjUTkJ0Z/7aWF8FYcHE1PQ7vT10JQEuLfTSLB45y8O3t7fh8PioqKsjIyFj4gDij96zsdOOdO3eOoaEhcnJyKCkpMbx9O2o2OTlJR0cHbrebmpoaw9u3o2bhIZqyilUJyaDRKcqbHmjVe/Cj/lIAWmWIxr7oF7wZr83h7drpxgvXzIgqkrOxo2YtLS0IIaipqSE1NdXw9vUefHNzs23SS88OaHWDczImycnNT0gGjU5u1nSBs/CqkikpHnp6umyVXhorjnLwZsZFAVasWIHL5aK1tRWfz7fwARbAbM3KysrIyMigr6+PoaGhhQ+wAGZ3JHJycigtLWVycpKuri5TbIiGQAAGRjSXW1GqFZ1JRAaNTk4muFzaQGt4VcllZSsB7cGYLDjKwZsZFwWtnGtFRQV+v5+2tjZTbIgWszVzuVyhtu0yaGi2ZuFt2+HNZ2DUTUC4wdtPRYU263fCC8UJcvBut4vifC1Lp7QAUj3aGEBR2QbAHprFC0c6eLN6VuFt2+UikppFj9QsOnoHg25mqouKKu3BJATkZiUuJLi8SJvR6naHp0peAthDs3jhKAdvdrghvG27XETSWUWPla4zO7z1nA1z8KEUyQRl0OgU57nwBZcLnF1V0g6axQtHOXgrOSu7XETSWUWPla4zOzwUzw6maD8Ec+ATmUGjM9dAq15V0g6axQvHOPiRkRF6e3tJT0+nrCzG9b9iwE6xUbPnDejYSTMhhIzBR0nvjB78qoRm0OhkZ4DbDf6ACDn4cTS/YAfN4oVjHHxrayugXfhGVvebjZ16VmbPG9Cxk2ZmzxvQsdNbT8857bvLd4bS5dUJzaDRcbtdlBTA+OR0D35oogDQNEuWssGOcfB61oqZr83h7dvBWVkh1ADMyKKxel53vOcNNDc38/Of/zz0+0MPPcQdd9yx4HFVVVV4PB46Ozstn9fd3qPVDcjP9OJJTU1oBk04yws1B6+nSp4Z9JCfX8DIyAhnz55NvAEWwDEOXu/Bm+2s7JTXrff+zAw1gJbXvWzZMqampiyf1x3v8MxsB79YUlJSWLFiRegcVqa7T3tolxRqsfhEZ9DoFOS48PuhJB8y0mB4DCpWaAOtduiAxQPHOHg9lmy2s7JTXrdVevDhNljlxhsdHeW9730vW7ZsYdOmTTz66KOsXLmSH/zgBwC8+uqrHDhwgGuvvZY1a9Zw7733AlqM/q677mLTpk1s3ryZRx99dN7Pv/CFL7B//362bt3K9773PUBbYWvPnj3U1tbyuc99LqKNVtMsEv3DHgAqSoNLNyU4g0YnIw1wafeknipZVLkDsL5m8cIxDt4qPfhwG6x+EUkHH5mnnnqKiooKDh06xNGjR9mzZw8Afr+We3fhhRdyyy238Nhjj/Haa6/xla98BYBf/vKXHDx4kEOHDvHcc89x11130dXVFfHze+65hyuuuIKDBw/y6U9/GoCDBw/y6KOPcuTIER599NGIk+bsEocfmtAc+4qqQkMyaHQy0kBfhVuPw2cWXQRYX7N44RgHb5UYfLgNVnFWkbCDg3e5XHH9qqqqWlTsfPPmzTz77LN8/vOfZ//+/eTn58/YvmXLFi699FJyc3NZtmwZ6enpDAwM8NJLL3HzzTeTkpLC8uXLufLKK3nzzTcjfj4XV111Ffn5+WRkZHDhhReG3k4Xq5mVGBkX+EQG+MdZUV3JpAEZNDrpqYRyJS9YoX1VlGlrDltZs3jiMduAeBAIBGZk0ZiNXUI0VonBh9tgFc3WrVvHgQMHePLJJ/nSl77EVVddBUx3JEpLS+ns7Azt73a741Z/KD19unubkpIS8bx2SJU8E8ygwdtNRdUqJqe0WaZG4PG4SE8V+PyCm69y8Z6LBaLfzSNYW7N44ogefHd3N5OTk5SUlJCbG+clYpaAHXpWw8PDoXkD5eXlZpsTUTMhRFy/2tvbF5Ui19nZSVZWFh/+8Ie56667OHDgAEBo7dhIKZJXXHEFjz76KH6/n97eXvbt28e2bdsifp6bm8vw8HBMmlnloTgX3f3BH6a6qKhcraVIGniL5mZpa7/qrFhhfc3iiSN68FaYjRmOHRx8eO/dzHkDOlbT7MiRI9x111243W5SU1P54Q9/yA033IDf76eiooK0tLQ5j7vxxht59dVX2bJlCy6Xi29961uUlZVF/Ly4uJiUlBS2bNnCLbfcQmFh4aJtDNdMCGFKueeFaGofAXJICfSRV1DMWD9kphtnZ142dJ6F4PAuVdXTFV+9Xq8p5Z4NJd49pBi+lswjjzwiAPGhD30oltPEjeHhYQGItLQ04ff75923vb3dIKtm8qtf/UoA4vrrr1/S8fG22+fzCY/HIwAxNjYW13OHE4vde/fuFYC4/PLL42jR4ohkd0FBgQDEmTNnDLZocXziG02CK/wir+4/xQsHAuI/n/WL7r6AYe2faPGLR/f6tbaf84uuswFRU1MjANHQ0DDvsWbdm0sgol81v+sWB6w0WAj2yOu2Uvwd7JHXbYUSBbOxehy+qX0UgIKc6XGENAM7zdkZLvyz5s5ZXbN44igHb6Ubz2ohh9lY7aEI1tfMaqFAsH4cvrNXc+zLCqajwWkGBoYz0qaLjulYXbN44ggHb+Ubz6rOSjr46JGaRU/vgOZi9JWcBMb24DPmGCqxumbxxBEO3oo3ntXS/mZjtRANWF8zK74pWj3cMDiuTVldUVVAICBIcUOqx7hB1oy00FynEFbXLJ7Y3sFPTEzQ0dGBx+OhqqrKbHNCWLmXEAgELOngrawZWPtN0aoPxXG/VlVs7crl+PzGzGANx+NxkeYBv3/azVtds3gSczRMUZRvAjuBZuAvVVX1Bj9PAX4E1AJvqar6N7G2NRf6gFxlZSUej3WyPq3srLq7u5mYmKCkpIS8PAPK+i0SK2tmtXkDOlbWbGLSR8BdBCLAujXVeP2QZUANmtnkZcNU2FwxK2sWb2LqwSuKsgWoVFX1CuAE8IGwze8DOoPbshVF2RFLW5HQn8I1NTWJOP2SsfJFZMWQFpyf120lEjVvYKnlgnVWrJiZ120lDh7rApcbl7+PzKwMfD7INrgHD1pphPDJTqWlpWRlZdHf38/g4KDxBhlIrFfqTuCZ4M9PAZctclvc0J2V1Ry8Xq+7q6vLcvW6rRieASgsLCQ/P5/R0VF6e3vNNmcGidJsqeWCddLS0qiqqppRrsMqHDreDUCGW6tXYJUefHjFVyt2wOJJrDGNQkBP9B4EimZtG4qwDQBFUW4FbgW444472L17d9QGDA4OUlVVRXV1NR0dHVEfn0gqKytpaWnh9ddfp7a2ds59vF6v4XYfPHgQ0KbbL7XtRNldXV3N4OAgr7/+Ou9617vifv7F2j02Nsbtt99OV1cXgUCATZs2AVotmo0bN+LxePjmN7/JPffcQ3NzM7fffjsf+chHEELw9a9/nRdffBGAT33qU7z//e+P+PlnPvMZGhoa2LhxI3/+539Ofn4+jY2N7Nq1i5aWFvbs2cOXvvSlee2urKykra2NN99809SVuWbz+tutQB256aOU5XRSnAk5bjD6Ns1wwQXBqNrECHRMQEVFBceOHUNVVUpLS+c8zox7cylUVlZG3Bargx8A9CBuPtC/yG0AqKp6P3B/8NclvZPffffd3H333XR0dMz7h5rBunXraGlpYXR0NKJtZtitr2azdevWJbedKLvXr1/P0aNHGRkZScj5F2v3448/zurVq9m7dy8At956a8i+xx57jE9/+tN87nOf4+WXX2ZiYoJNmzbxhS98gccff5zGxkaOHTvG2bNnueSSS7jhhht45ZVX5vz8u9/9Lt/5znf47W9/C2ghmpMnT/L222+Tnp7O+vXrufvuu0lNTY1o94YNG3jttdcYGhqy1D2Q4uuAju9zwbZqukfeRXef4N1bXVSUGFtSoXdA8Hq9VqZ411YXZcUuLrzwQp599lkGBwctdW/Gm1gd/CvAZ4BHgGuBl2dtuxrYF9z2YIxt2Q6rxuGtGoOH8zVzvTveS/iVAwHEvvmjk5s3b+azn/0sn//853nf+94X6sldd911oe0jIyPk5uaSm5u75HLBcw1y6+WCgVC54PlCQ1a9zkZ6XoXTv2DPrd/XPnAZO8lJJ5lz4WOKwauqehDoURRlP7AReFxRlPuCm38L1AS3Taiq+mpMltoQq+Z1WzUGD9bRTC8XvHnzZr70pS/xxhtvALBmzRpAKw8cXtbXjHLBOlbRbDa6PZVV2viYC2MnOenMlwtvNc3iTczPU1VV75r10W3Bz33ALbGe385YsZegzxtISUmhurrabHPOY7ZmC/W0o2Wxr92dnZ0UFRXx4Q9/mLy8PG688UaAUL2cSFxxxRXcd999fOxjH6O/v599+/bx7W9/G5/PN+fnHR0dSy4XrGPF6wym7amq1hy8wJwefKrHRapHzMiksapm8cY6ieMOxIoXkT5vYMWKFZaaN6BjFc3CywWDNjnM7XYvuN6AkeWCdayiWTj6vIGMjAxKSkrpn9Q+TzXpksvNgq6z07/rPfjm5mb8fj8pKSnmGJZo5is1afBXTFixtGd/f78ARHZ2tggE5i6RarTdv/vd7wQgrr766pjOkyi7JycnhcvlEm63W0xNTcX9/Euxe//+/QIQ27Zti7s9i2U+uwOBgMjKyhKAOHfunHFGzcOhQ4cEIGrXXSCOHG8Xz73pF4+/OH/p7ETyyhG/uPcJrVywzvLlywUgWltb5zzGij4lAs4uF2xVwvO69cwVs7Fy/B2smddtdc3C87qtElPW7VixUnu78Pohy8QMzrxsCK6XHiIZShZIB59grPb6bOUMGh2pWfRYzVnpmq1coT14zJrFqpOb5YpYNtgq11kikA4+wVjtIrKTs5KaLR6rajajB2/CLFadjDSYvcqi1TRLBNLBJxir9aysWBFxNlKz6LHa1PtQiEbvwfsh28QQTUba+Rk8VgtrJQLp4BOMlW48IYQla5rPxkqagTXrwM/Gar3R2T14vx+yMsxbFDwj7fwcfKtplgikg08wVrqI+vr6GB4eJi8vj6Ki80oDWQYraWb1eQM6VnrrCV9voCbYgzdrFqtOquf8Ga1Wus4ShXTwCcZKF1F4LNnlMq83tRBW0szq8wZ0Vq5cCUzndZtJ+HoD+rwBs2ax6rhcLnJnjQFUVFSQmppKd3c3Y2Nj5hiWYKSDTzB6ve62tjbT63XbIZYM0/W6z507x8DAgKm22EWz7Oxsli9fztTUFJ2dnabaEkkzM3vwoE12CiclJWXGg9GJSAefYKyU122HWDJYK6/bLpqBdd585so6Mnqx7bmoKHGROStV0yqaJQrp4A3AKheRHdL9dKRm0WOVOPxszfRCX2aVKdCpXOYiP2dmaNIq11mikA7eAKxyEdnRWUnNFo9Vso9mv/UIAZnpWHLcxyqaJQrp4A3AKj0ru8STQWq2FKzyUJytmRDmzmKdD6tcZ4lCOngDsEIvwev10traisvlWrDkrRWwgmZ2mTegYxUHf95bjzB3Fut8WEWzRCEdvAFY4SJqa2vD7/dTWVk5Y0EJq2IFzewyb0DHCgPT4fMGqqqqAC0Gb9UefHhHQoglrRpqaaSDNwArvAbaKdQA0zdeS0uLaXnd4ZpZMX48m8rKStPzultaWoDz5w1kW7QHX1BQQGFhIWNjY/T29pptTtyRDt4A9Lzu/v5+0/K67TRYCJCVlUVZWZmped120yw8r9uszsRcmrlckJFm3QekFd4WE4V08AZghbxuO8WSdcyOw9tRM7OdVSQHb3YO/HyYrVkikQ7eIMy+iOzWGwWp2VKwYkfChfmzWOfD7I5EIpEO3iDMjsPbLQYPUrOlYPZDcS7N7NKDd2KqpHTwBmH2jWfH3qjULHqsqJnL5EqSC2G2ZolEOniDMPM1cHBwkL6+PjIzM1m+fLnh7S8VMzWz27wBHTM1izRvwOUCj8e6g6xmh2gSmZ4pHbxBmNlLCF802g7pfjpmama3eQM64ZoZndcdad6A2+KXXE1NDW63m7a2Nqampgxte3JykoKCAhRFIRAIxP380sEbhN5LaG5uxufzGdp2Y2MjYK9QAxByrj09PYyMjBjatl01KygooKioiPHxcbq7uw1tO1yz8I6E2+JeJi0tjZqaGoQQhpcNbmxsZGhoiIGBAdwJEMri0juHrKwsqqqq8Hq9ockgRnHq1CkA1q1bZ2i7seJ2u1mzZg0A9fX1hrZtV80Aamtrgem/wSjm0iwrw9rxdx2zNNOv60RdZ9LBG4h+ERntrPT29PbthNQseqykWWa6y/QywYvBSprFE+ngDUR/SluhZ2UXpGbRIzWLHrM1kw7eATi1l5BIpGbRIzWLHrM1kyEaB2DGRTQ4OMiZM2fIyMigsrLSsHbjhRmaeb1empqacLlcoTEAO6E7CyM1E0JIB78EZA/eQZjxGhh+0yVilD7RmKFZU1MTfr+fmpoaMjIyDGs3XujOoqGhISGpd3PR09PD8PAwhYWFFBcXG9JmPFm5ciUej4fW1lbGx8cNaXN0dJTOzs5QFk8isN8db2NWr16N2+2mpaXFsHxbO/eqAMrLy8nOzqavr49z584Z0qbdNcvNzaWsrIzJyUna2toMaTNcMzvNtdBJTU0NpTLr6Z6JpqGhAYA1a9aQkpKSkDakgzeQtLQ0Vq5cSSAQMGzyTqJjfInG5XIZ/vpsd83A+JCDnQdYdYx+W0x0eAakgzcco/NtjbiIEo3ULHqM1szubz1g/EPRiI6EdPAGY9ZFJG+8xeMEzYweaJUPxeiRPXgHYuRroBBCvjovASdoZlYP3s6aGf1QlD14B2Jkb7Svr4+BgQFyc3MpLS1NeHuJwkjNJiYmaGtrm7H8nR0x0lkFAoHQgKETevBOeuuRDt5gjLzxwnsIdsxs0AnXLNEVEhsbGxFCsHr1alJTLbxKxQLo+ftNTU14vd6EttXe3s7ExATLly8nLy8voW0lkurqatLT0+nu7mZoaCihbQ0MDHD27FmysrKoqKhIWDvSwRtMTU0NqamptLe3MzY2ltC2nBAXBSguLqagoIChoSHOnDmT0LacollmZibV1dX4fL6EV0h0wpgFaMXt1q5dC0ynMCYKo9JKY3LwiqJ8U1GU/Yqi/ERRlNRZ23YpitKmKMqLiqLsjc1M5+DxeEIlaI28iOyMkamSTtEMjHtbdMpDEYwbuzBKsyU7eEVRtgCVqqpeAZwAPjDHbo+qqrpLVdWrltqOEzHqxnPCwJeO1Cx6jH4oOkEzp11nsfTgdwLPBH9+Crhsjn3+LNjD/1QM7TgOp/USjEBqFj1GZR856a3HqIeiUddZLJWaC4Gu4M+DQNGs7SqwPvjzE4qivKSq6lvhOyiKcitwK8Add9zB7t27l2yM1+ulo6NjyccbybJlywA4dOhQwuwOT5HMzs6OextG663XNzl8+HBM7S5k98mTJwFtur+Vrqel6K0vm3f06NGE/i3vvPMOAHl5eXO2Y6d7s6CgANA0S6TdumYFBQUxtzFfEcEFHbyiKGXAL+bY9AygD5nnA/3hG1VVDa2xpijKb4AtwFuz9rkfuD/4a0zpER0dHbaplrht2zZAyz5ITU1NiN2dnZ2MjY1RXFzMpk2b4n5+o/Xevn07oK2VGku789k9PDxMT08P6enpXHLJJQmrD7IUlqL3jh07AGhpaUnY/8rn89Ha2grAZZddRlZW1nn72OnevOwyLRDR0tKSsHszfGnAyy67LNThSwQLOnhVVbuBXbM/VxRlK/AZ4BHgWuDlWdvzVFXVc40uB+6N0VbHYMRroJNem+H8ComJqIxpRPEnI1m1ahUpKSm0trYyMTGRkMqY+hrDVVVVczp3u1FWVkZOTg79/f2cO3cuIQ6+t7eXwcFB8vPzKSkpifv5w1nyXaKq6kGgR1GU/cBG4HEARVHuC+5yk6IobyiK8grQoarqvliNdQqVlZVkZmZy5syZhOXbOmngCyA/P5/S0lLGx8fp7OxMSBtO0yw1NZWVK1cihEhYcTunaRaesWWEZomenxLTaomqqt41x2e3Bb8/ADwQy/mdip5ve+TIEZqamtiwYUPc23DSYKFObW0tZ86c4dSpU1RVVcX9/E7UbN26dTQ2NnLq1CkuvPDCuJ/fiZrV1tby9ttv09TUlJDzG6mZnOhkEvo/N1EXkdNCNJD40JbULHqcrJkT7k3p4E1Cf6VN9EXklFdnSHyOstQsepysmRPuTengTSKRcb7w4k/61GsnkOhceKeGG0BqFg2JjsHLEE0SoD+9E7E8WEtLC5OTk5SXl5Obmxv385uFrtmJEyfifu6+vj76+vrIzs6mvLw87uc3i0RqNj4+TmtrKykpKaHyG05A1+z06dNxX9PW7/fLEE0yoOemnzx5Ep/PF9dzHzp0CIDNmzfH9bxms379ejweDw0NDYyOjsb13OGa2bny5mxqamrIzc2lp6cn7oXajh49SiAQYP369aSlpcX13GZSXFxMeXk5Y2Njce/FNzQ0MD4+TnV1dWhSVSKRDt4kCgoKWLFiBZOTk3GPj+rOasuWLXE9r9mkp6ezYcMGhBAcPXo0rud2qmZut5uLLroImP4b44VTNYPpv8numkkHbyJOuYiMRGoWPVKz6HGKZtLBm4hTLiIjSZRmBw8enHF+J5Ho62zr1q1xPa8V0P8mu9+b0sGbiP5P1p1LPBgaGuL06dOkpaWxfv36hQ+wGYnQbGpqKlT8yWnjFpAYzYQQSdGRiKdm4eeTDj4JSEQv4fDhw4A2iGvnJeciod8Yhw8fjluGw4kTJ/B6vaxdu9ZRWUc6mzdvxu12c+LECSYnJ+NyzubmZoaGhigtLaWsrCwu57QStbW1pKen09rayrlz5+Jyzr6+Pjo6OsjOzg4tqZhopIM3kVWrVpGdnU1XVxe9vb1xOaeTe1UApaWllJeXMzIyEreJKE7XLCsri9raWnw+X+hNJVacrpnH4+GCCy4ApjtNsRKeqWVUMTvp4E3E7XaH6tDEqxfv9BsP4h9TlppFTzJo5oR7Uzp4k9ELQNn5IjIa6ayiR2oWPU64N6WDN5l4XkR+v58jR44Azr7x4umshBCOzqDRkQ4+eqSDl8RMPF8Dw2fJFRYWxnw+qxJPZ9XV1cXZs2cpKCigpqYm5vNZlXDNhIhp8bQZmVp6nNqJ6A7+6NGjMc8293q9pmRqSQdvMhs2bMDlcnH8+HGmpqZiOlcy9KpAqxWSnp5Oc3Mzg4ODMZ1L1+yiiy5yVImC2VRWVlJUVER/f3/Ma4Dqb4kbN250ZKaWTl5eHitXrmRycjLmYm0nTpxgamqKNWvWGJqpJR28yWRlZbF27Vq8Xi/Hjx+P6VzJ4uA9Hk+olk+sGQ7JopnL5Yrbm0+yaAbxe1s0SzPp4C1AvCZVJEMsWSdemiWjs5LX2eKxu2bSwVsAu/cSzEBqFj1Ss+ixu2bSwVuAeMxoNWOWnJnEQ7Px8XFOnjxJSkoKGzdujJNl1iUemiVLppZOPBx8eFkHo+v2SAdvAeKR4WDGLDkz0UvgxpLhEF7PPDMzM57mWZINGzbg8Xior69fcj19PVOrqqqKoqKiOFtoPVatWkVOTg7d3d1Lrqff3d1Nb2+vKZla0sFbgKqqKgoLC+nr66Ozs3NJ50im12aYrqc/MTGx5Hr6yaZZPOrpO7mC5Fy43e6Ye/FmZmpJB28B4pHhkGzOCmJ/fZaaRY/ULHrM1Ew6eItg54vILKRm0SM1ix47ayYdvEW4+OKLAXjttdeiPnZ4eJhjx47hdrsdWc88ErFoNjk5yYEDB4DkCTdAbJoFAgHeeOMNQGq2WIQQoePM0Ew6eItw1VVXAbB3796oa3bv3bsXr9fL9u3bHVnPPBK7du3C7Xbz0ksvMTQ0FNWxL730EqOjo2zatMmR9cwjsXPnTjIyMjhw4ADd3d1RHfvWW2/R29tLTU0Na9euTZCF1uPiiy+moKCAhoYGGhoaojr21KlTNDU1UVxcLB18MlNVVcXmzZsZHR3lpZdeiurY3//+9wBcd911iTDNshQWFrJjxw58Ph979+6N6thk1SwzM5Ndu3YB8PTTT0d1bLhmTi7rMBuPx8M111wDTGuwWPT9r7nmGlOy26SDtxDXX389EN1FJITgySefnHF8MrEUzcL3l5otHqmZDTUTQljlKyba29tjPYUphNv94osvCkBs2LBh0ccfOXJEAGL58uXC7/cnwsQ5sYreBw4cEICorKwUgUBgwf3b29tFc3OzAERubq6YmpoywMrYiafe9fX1AhAFBQXC6/Uu6pje3l7hcrlEWlqaGB4ejqo9q1wr0RJud3d3twBERkaGGBsbW9TxIyMjIi0tTbhcLnHmzJlEmSnEPH5V9uAtxM6dO8nLy+P48eO0tLQs6hi9h7Bnzx7c7uT7d27dupWysjI6OjoWnduta3b11Vc7uhpiJNauXUttbS0DAwO8/vrrizrmmWeeQQjBFVdcQU5OToIttB7Lly+nrq6OiYkJXnzxxUUd88ILLzA1NcUll1zCsmXLEmtgBJLPI1iY1NRUdu/eDSz+VTCZwzOgzSHQ4+iL1cz012YLIDWLnqVqZuY4j3TwFkO/GHTHPR9DQ0O89NJLuN3u0IMhGYlGs8nJydCA7J49exJql5XRHfViNAsEAjz11FNA8g1KhxONZsIiY2PSwVsM3eksJl3yueeew+fzsWPHDkev4LQQu3fvJiUlhZdffnnBBUDeeOMNRkdH2bx5M1VVVQZZaD2uvPJKMjMzefvtt+nq6pp3X1VVOXv2LCtWrHD0Ck4LsW3bNoqKimhsbFywPMbJkydpbm6mpKQERVEMsvB8pIO3GJWVlWzZsoWxsTH27ds3777ytVmjoKCAnTt34vP5eO655+bd9/nnnwekZhkZGbznPe8BCPXOIxHeE02m9MjZpKSkhNIlF+rF69uvvfZaU8fGpIO3IIuJ9QkhLBHjswqLjY++8MILM/ZPZhab+ievs2mi1cz0jsR8KTYGf8WEE1KxdP7whz8IQFxwwQURjzt06JAARFlZ2aLSA+ON1fR+++23BSAqKioi6nH69GkBiLy8PNukR+okQu/GxkYBiPz8/IjpkmfOnAmlR46MjCypHatdK4tlLrt7enoEINLT08Xo6Oicxw0PD4fSI3t7exNtphAyTdJe7Nixg/z8fE6cOEFTU9Oc++ivgMk2qzASW7Zsoby8nM7OzojrtOq9qt27dydleuRsVq9ezfr16xkcHOTVV1+dc5+nn34aIQRXXnkl2dnZBltoPUpLS7nkkkuYnJwMvQ3O5vnnn2dqaopLL72UkpISgy2ciXTwFiQ8XfKBBx44b/v4+Dg/+9nPAPnarBOeLjmXZl6vl4cffhiQmoUzn2Z+v58HH3xwxn6SaS1+/OMfn7dAjxCCH//4xzP2M5X5uvcGf8WEk14DhRDiueeeEy6XSwDil7/8ZejzQCAgbr75ZgGIFStWRD2rMF5YUe9XX31VpKSkCEA8/PDDoc8DgYC47bbbBCBKS0tFX1+fiVYujUTpffjwYZGeni4A8W//9m8ztt11110CEIWFhaKjo2PJbVjxWlkMkeyur68X2dnZAhD/9E//NGPb1772NQGInJwc0djYaISZQszjV5fskOvq6vLr6ureqKurG6mrq9s0x/aUurq6/6irq9tfV1f3/UWcMyacdhEJIcQ999wjAJGVlSXefvttIYQQX//61wUgsrOzxaFDhwyy8nysqvcPfvADAYi0tDTx8ssvCyGE+Nd//ddQ3PR//ud/TLZwaSRS75/+9KcCECkpKeKZZ54RQgjx4IMPCkB4PB7x/PPPx3R+q14rCzGf3b/+9a9DHbBf/epXQgghHnvsMQEIl8slfvOb3xhkpRAiQQ4+ta6ublldXd1DERz8n9TV1X09+POP6urqdixwzphw4kUUCATERz/6UQGI6upq8cMf/jB0AT3xxBMGWnk+Vtb7r//6r0O99QceeEC43W4BiJ/97GeWtns+Em333/3d34Xq0zz44IMiLS1NAOLee++N+dxO1fwb3/hGqLP10EMPiaysLAGIb33rWwZZGCL+Dl5MO/JIDv6bdXV17w7+/Gd1dXV/u8C5YsKpF9H4+LjYsWOHAEJf99xzj0HWRcbKenu9XnH11VfP0OyLX/yiEMLads9Hou32+/3ihhtumKHZnXfeGZdzO1XzQCAgPvKRj8zQ7GMf+5gZWW0R/apLiJmDBNGiKMpDwHdUVT066/P7gR+oqnpQUZSrgT9SVfXuWfvcCtwKcMcdd9TFMt3e6/XaMjNiMXb39vbyvve9j46ODj7wgQ/wve99z/TMGavrPTAwwB//8R/T1NTEnj17uP/++3G73Za3OxJG2D06OsoNN9zA8ePHefe7380jjzyCx+OJ+bxO1nxiYoKbbrqJAwcOoCgKjz76KOnp6QZZqFFZWRnRGSz431MUpQz4xRybPqSq6nxLwgwAecGf84H+2Tuoqno/cH/w15ieNB0dHVRWVsZyClNYjN2VlZW8+uqrvPDCC3zwgx80/AKaC6vrXVlZycsvv8zTTz/NBz/4QTIzMwHr2x0Jo+zet28fv/nNb/jABz4Qt9XBnK753r17+dWvfsWNN95IQUFB4g2LggUdfNCJ71rCuV8Brgb2AdcCDy7hHJIg1dXVfPSjHzXbDFtRXl7OLbfcYrYZtqKkpISPf/zjZpthKwoKCiyrWUx58IqiPAlcA/xIUZRbgp/dF9z8W6BGUZT9wISqqnPPpJBIJBJJQogpwKaq6nmFFlRVvS343QfcEsv5JRKJRLJ05ExWiUQicSjSwUskEolDkQ5eIpFIHIp08BKJROJQpIOXSCQShxLzTFaJRCKRWBPZg5dIJBKHIh28RCKROBTp4CUSicShSAcvkUgkDkU6eIlEInEo0sFLJBKJQ5EOXiKRSBxK7Mu1GICiKB7gI8BxVVVfM9ueaFAUJRX4C+CUnUom21Vzm+v918A7wCuqqo6abNKiCOp9O1APvKSq6ojJJi0au17j0WD5iU6KopQCPwKa0VaGehp4SlXVc2batRgURbkS+D7wKlAEPAA8r6pqwEy7FsKumttY7wLgu8AwMBb8/qCqql1m2rUQiqJsB36AtriPFzgO/FRV1TFTDVsEdr3Go8WyIRpFUfR16caBDlVVPwV8EygDPmCaYYtEUZQUIAO4TVXVTwC/A65SVTWgKIq5C6pGwM6a21RvfUnLYSAN+DLw1eDvnzDLrsUQ1HQK+ISqqncAvwZWqao6ZlW9wd7X+FKwXA9eUZQS4BtAH7AXeAv4FPAw0ALsQFsC8MeqqjabZOacKIpSAXwB+I/gYuOFwICqqkJRlGq0XtpfqKrqNdXQWdhVcxvrXQT8E1qI9PfAU8D/BxxTVfW54N/1D8C/q6p6yDRDZxG06zvAv6iq+rqiKHmqqg4Ft+WiOfk/tmIP3q7XeKxYsQf/AbQFu3+B9g9YBeQCl6DZ2wC40Ho8liH4yvdV4ELg6wCqqp5TVVV/gm4HWqzmbILYTnOb670HLaTxDeC64O+pwEpFUapVVe1EC3eUmWfiTBRFyQE+CawAvgSgO/cg29Bi2ZZz7kFsd43HA0s5eEVR3MBy4Beqqh4EHkP7B6hAHdordzdQDWSZZWcE+oF/UFX1amBSUZQPQ2gQCrQb+NeKovyJoii3K4qSbZah4dhYc1vqHSQdeFFV1UbgZ2iOfBTIAf40uM+q4GemEhZuGQP+WVXVy4CpML11h5gG7FUU5QZFUe5WFGWZCebOiY2v8ZixjINXFMUVHAwbAv4q+PEvgRq0p+sTwOXBhb5TgW5TDI1AcA1a3aZvAJ9QFCU9rAd5A9rN/FHgDbOyJIKv0vrPttE83G6wld5z9cIngcuCP7+GFsvuAX4DlCiK8ms0Z9NmhI1zodutvxEFrxN9ADJc76ngZzegDVp+CHhaVdVeYy2eRlGUqrCf3Xa5xhOBKTH4YKz0+2gj7s8GB8gC+sWkKMo+4HOqqr6mKMr/Ai5XVfX/BHsLdWamv81lu6qq/rDtrmAM+F+AJlVVv68oSj7wWeCoqqr/ZZLdBWhx3wzgBVVVfxJ28VtW87nsnrXdynr/I7AJzYn8QlXVM2HbnwD+XzDm/h7gQ6qq3ha8Fy5UVfWIRe2eS+8y4L3AuKqqPzfD7jD7rkG7Xr6qqur/KIriCXYGLHuNJxLDe/CKomSixfJWAX8HoKqqP3jRpAR3+1fgbxRF2QQUA42KoqSpqjplsnOf0/ZZu+mvtF8GvqooykGgVFXVvzfR2SwH7kHrJX4Trfe1Jphhooc0LKd5JLuD23Sdrah3PnAXWsz348BmNE3DQ0j/DtygKMr70cYRRhRFyQreC2Y59/nsnkvvrymK8hZQqarqj8127kHOARPAnyuKkqOqqs/K13iiMawHryiKArSrqtqtKEqJqqpnFUV5EDigqur/m6Mn/D60ke1a4FNm5gQvwfYCtIGcDcDdqqqeNtHuVlVVzyiKslFV1WPBz7+Klm3y3Vn7W0LzJdhdgHX01q+TXFVVh4Of/ztwGHhIVdXJsP3fhZa5sR74oqqqHTaxOwO4Ey2O/XlVVZvMsDtoSx3QFRyYRlGUS4B1QDmQqqrqN8LvT6tc40aRcAcf7JX/O9rN1wS8rKrqj4LbatHipNepqtqnv/6FHztHD9kwlmp7sMdQYqKDDLe7GdivquoDwW0utPDFW6qqvqB/Fma7aZpHa3fYcVbSuwltJur9wVf/jwL/GzgGnAIetopTicVuRVEKVRMnBc2y/TTwuqqq9yra5KtbgS+ivf39EC27ZzD8WDP9ipEYEaLJAvpVVb0SLa3tOkVRLgjGf+vRcoA/Gdy3NPxAC/wTlmS7qqpek2/icLv/EbheUZQLgrYJYCVwUlGULEVR8sMfqiZrHpXd+kEW0/urwB5FUS4IDkA+q6rqpWghvUK0AVarsGS7zXTuQcJt/xpwraIourN/Ci3Lqhi4D83+EBbwK4ZhhIN3A+9WFGVF8NV5H3CTPrinqurfA3cqinII2GiAPdFgV9vntBtC2SgbgPehTfjYapaRc+Akuz8IoKpqS3Cf64ELsFb9J7vaDefb/gfg/cAy4G/QUiGPodXIseJcCEOIq4MP71UFf08Nvhr9EvhK8OP/AK4MPm1RFOX/AC8BN6uq+nw87YkGu9oepd0XAilog8Q7gL9SVfUPRtobZqfT7X63oigbgm8bP0CLtd+lhmWkGIld7Q7auljbrwXOAt8G/lZV1c+j3ZumjG1YgbjE4BUtTeo+tLzd76iq2qyEpeAF93kW+CdVVV9QFOULaNOyf6MoSoGqqgMxG7FE7Gr7Euz+O7Tp2W8AG1VVfVnanXC7DwIvACtVVT0h7Y6OJdj+ReBtVVWfDG6bMaaXjMTLwf8p2tPzCDCoBnOVgwMh/4zWyx1HK+PaDVyO9mQ1JdshHLvavkS7/0LVZk+aRpLZbdfrxHS7Ycm2f0g1MavHaizZwSuKchPa4MVP1GD9CUVR9gDvQiu7eUBRlEvRRuO/rKpqv6Io5cBOYJ9q7kw3W9ou7ZZ2O9luu9tuRaJ28IqWrvY3aMWcTgJ+tHS254NC/ymAqqr/Puu4Ga9WZmBX26XdxiLtNh47225loh5kDca0ctGesH+PVkvjo4o2C68LeBsQiqJ8QFGU6yEUCzP9n2BX26XdxiLtNh47225llppFcwJYpihKFvAc2iSJDwW3vQ1cjVaQqBKmCxZZBLvaLu02Fmm38djZdkuyVAd/DG0q8DpVmzTwFloxKIDdaFXadqnBWZ8Ww662S7uNRdptPHa23ZIsdfLCO8AVaLPHmoACQK+3/aSqqv8TB9sShV1tl3Ybi7TbeOxsuyVZUg8++Gr0CNAJ/BitzvLh4DZf3KxLAHa1XdptLNJu47Gz7VYl5jx4RVE2AqdUay6NNi92tV3abSzSbuOxs+1WwnKLbkskEokkPlhmyT6JRCKRxBfp4CUSicShSAcvkUgkDkU6eIlEInEo0sFLJBKJQ7HaKi0SScIJToX/HNCsqupDiqLcAjyItrDFd0w1TiKJI7IHL0lGstBWArol+PsfgJuB35hlkESSCGQPXpKMqMHvVyqKIoAWYAVwF9qi3s1ACfAw8GG0hSX+Dbgf7Z75uKqqTymKkgb8E9rDIRt4FviErEkusQqyBy9JRu4Ofj+O5pznCsvoNVBeRVt4+odoa32WAvcEt/0d8Fm0nv/3geuAexNisUSyBKSDlyQjzwS/n1FV9RfAyBz7BIBPA48Hf/+Jqqr/ilYnZVXws/cFv9+GFvLJRqt6KJFYAhmikSQji6nPMa6q6pSiKHotlMHgdz+QErafD83R+4O/y06TxDLIi1GSjAyh9dDXKoryv9Di70vht2idpI8BNcAetN68RGIJpIOXJB3BCoXfRqs3/lOme9/R8o3gea5AG4S9Di0jRyKxBLKapEQikTgU2YOXSCQShyIdvEQikTgU6eAlEonEoUgHL5FIJA5FOniJRCJxKNLBSyQSiUORDl4ikUgcinTwEolE4lD+fwGqUANASlu6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_series = [sine_series, irregular_series]\n",
    "for series in train_series:\n",
    "    assert not series.has_static_covariates\n",
    "\n",
    "model = TFTModel(**get_model_params())\n",
    "preds = test_case(\n",
    "    model,\n",
    "    train_series,\n",
    "    predict_series=[series[:60] for series in train_series],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0c99b81-c62a-4712-8b53-57adfaae4b10",
   "metadata": {},
   "source": [
    "From the plot you can see that the forecast began after period 3 (~01-03-2022 - 12:00). The prediciton input were the last `input_chunk_length=10` values - which are identical for both series (sine wave).\n",
    "\n",
    "As expected, the model was not able to determine the type of the underlying prediciton series (smooth or irregular) and generate a sine-wave like forecast for both."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb22bf06-9803-40f6-9d37-25e65840a4ad",
   "metadata": {},
   "source": [
    "### 6.3 Forecasting with 0/1 binary static covariates (numeric)\n",
    "\n",
    "Now let's repeat the experiment but this time we add information about the curve type as a binary (numeric) static covariate named `\"curve_type\"`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "44b34b11-d5de-4d6d-a817-b584b12fb12a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "static_covariates  curve_type\n",
      "component                    \n",
      "smooth                    1.0\n",
      "static_covariates  curve_type\n",
      "component                    \n",
      "irregular                 0.0\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9a5bf463e99b4c0baecc2944a40a0f65",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "259476d0a70f41eca31008b1ecdaa42e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 4it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EVDdN0hj88PAwR48exefzsXr1zDWm58KtN950BINB3nnnHUAft1goyaSZlHJicFQZ/KUMDkt8U1ynYSJF8jDLVlwxsXxgSFKSD9fXeyjMnTnV1OimuXxJDgDtfVl4PKpkASSRwR88eJBQKMTKlSvnVUtlJtx6403HyZMnGRoaoqqqKqpJYVNJJs06Ojro7OwkLy+PmpqaBR/HrYPT/cOQ4p+87GSz3vL2jhylum7lxPILQ7CmzoPXO/s8gpxMyM2EdasqABgIVhAIBJTBk0QGH4/HZoC1a9fi8Xg4fPgwo6PuTraNl2YrVqwgPT2dxsZGent7Y47LzkRqFs0Ep6lUVlZSVFREV1cXra2tcYrOegamMfhjjUMALMofJCUlFdD71LMzoKxw7mN6PB6WVsKi0lyQQUhfRlvTCXoH3PPFuFCUwUdJVlYWK1asIBAIcPjw4dgDszHx0szv90/MO3D74HS8NHPr4PTAEKRGGLyUkpYu/aUeK2ouvtyjdwDWLmHO1rtBeZEHvxcyPB3g8XHwaANdvfGM3Jkog18AbrzxpkNpFj1Ks5mRUjIwAv6IKgRdF2A0kA7j3axeWQfolSZT/LC4dP5PQLlZ+r/ibD0/8tjpPrr64xq+I0kKgw+FQpMmOcWK2268mVBmFT2J0MwtM1rHA3oFychW+cUaNAdZftkVAHRfgMtrIcU/f4M3umkqS3RLazrnYXgUxsaTu5smKQz+1KlTDA4OUllZSUlJSczHSwaz6ujooKOjg9zcXGpra2M+XjJoNjAwwIkTJ0hJSYmqguRMuE2z0fFLSxScag3pHwYPsXTFeoIhiZRQVx79+EVpgYdVS/VO+/OD+SBl0g+0JoXBx7NVFXkct2U4RBJrWYepGIPThw4dYmxsLObj2ZEDBw4gpeTyyy8nNTU15uNddtllpKWlcfr0aS5ccP7UzNExmHq3HDypz0jKTe0gOyeP7guwoopp893nIjsDVi4tBiCQspyernYGhmKN2tkog18AixYtoqysjAsXLtDY2BiXY9qNeGuWk5PDsmXLGB8f58iRI3E5pt2It2YpKSmsWbMGwBWajU5TVud0q/5uhZoy3fqDQahdQOsd9NmxSys9eOQ4pNdx+uQBepO8Jo0y+AXitsfnqSjNoieRmh06dChux7SKoRE5qYsmFJKcu6BPTlq9LBfQW/iZaZfuO18qiiE75Tx4vBw53ka38x98YkIZ/AJRZhU9SrPocZPBT53kdK4HAjINxtpZs/oypJR4PJAWQ+9WcR6U5ekd7yebh+hJ8kwa1xv8uXPnaGtrIzs7myVLlsTtuG42q8HBQY4dO4bf74/LYKGBmzULBAJxKeswFUMzN8y5mJoDf9rIoBk8xLIVVzAegOx0Ypoglpvloa5SL2vd1p3O8KiedpmsuN7g4z1YaOBms4q1BvxMuHlwOtYa8DNhVC89duyY42vDT53Feuik3n+SEjhJ6aIqxsYhJyu2c2RnwOXL9YHWgWAFI8P9DCVxJo3rDT4Rj83g7trwidLMzbXhE6WZURt+bGyMo0ePxvXYZiLDKYuRBn/0tG7wi/KG8Xg8jAX0ujKxkJkOteXht4lkrqbp9AFl8G4mUTeem2vDJ0ozt06/h8RpFnlMJ2s2HtAzZCInObWd13Pgaxfrxf/GAnrRsFjweDysqgUv45Bezcnjh7kw5K6nxWhwvcEbg1MLeQfrXES+Ws1NKM2iR2k2O6PjMLVrvWdI749ZFc5dR0Jm+sL73w3KCyE/vQeAE2d6kzqTxtUGHwqFOH78OKBPGok3xjGNc7gF4zVxK1eunGPL6FGaRY8bNBudMrdtLCAZDRWADLL6svA7WD2QlnLpvtFSmOthUaE+XtF23pvUmTSuNvjW1laGh4cpLS0lPz8/7sdfsWIF4Owbbyq9vb2cO3eOjIwMKisr4358N2o2NjZGQ0ODXg9l6dK4H98Nmo2O63VoDM52Ax4vjDZTU7d8Ynl67BOAycqAugo9OeD8QA79QxAMJmc3jasN3rghjBsk3rjhxpvKiRMnAH0QOZ5ZRwZu1KyhoYFgMEhNTU1cs44Mli/XDfDkyZMEg8G4H98MhkflpEI0x0/riQm+QAt5+RdfJhOPFnx2BtRW5evnDRUzMjzEsLtf3TAjyuBjoK6uDp/Px5kzZxgZccdQfaI1W7RoEdnZ2XR3d9PV1ZWQc5hNojXLzs6mrKyMsbExx2YfTc2BP9ag/+1zUvS+8kBQkuoHfxQVJGcixe9hSXm4JnF6LR1tJxlSBr8wNE37hqZpr2qa9pimaSkRy6/TNK1Z07SXNU17IdbzLIRE33ipqanU1dUhpeTUqVMJOYfZJFozj8fjulZ8ojUDJibpOVWzqbNYG1v1KmDFuXpf+XgcUiQjWVUb/pC+hPaW4wwOqy6aqNE0bT1QKYTYDBwF7piyyeNCiOuEENfHcp6FYsaNp8wqepRm0eN0g586yakj/PBWWaovHBuPr8EvqwSfZxT8ebS0NNOdpAOtsbbgrwaeC3/eBmyasv5D4db9X8Z4ngWRyAwaAzdkOESiNIseMzQzBm+dqtlUg+8e0t18abVebGwsoL+RKV7kZXvITR8AoKm1l54kTZX0z73JrBQARkWJPiDyFbkCMK74X2uatksI8Xbkzpqm3Q3cDXDvvfeyZcuWBQcyPj4+6eXEkZkN6enpCXtxcWlpKaC/dWch55gat5VIKSfS/bKzs2eNK5a4i4v1vOf9+/eb/rsnQm+jlG9ubm7Cfp/q6mpArzlvl+tlvoyPj7NyUfskgx8OFoMPrlnnZ1F2G0XpkOGBeP1qIQm1pZKeM3Cue4RFOe1RH9tO9+ZszJbtFqvB9wK54c95wMScfSHEgPFZ07SngPXAJIMXQjwMPBz+MaZOstbW1km/6NGjRwkGg9TV1cW1yNhU3vWudwHQ0tKyoLTCqXFbSVtbG4ODgxQVFU3UIZ+JWOLeuHEjAE1NTab/7vHWu7+/n7Nnz5KWlsaGDRvw+Xxz77QAjO6fxsZG21wv86W5uZVDreUsKtIHUC8MBAj5fBAcZlG1RsdAJh1dkvfWeygtiH2QFfTGSmFBP5yBjt5MDraW8wfXeKJ6kYid7s2FEmsXzW7ghvDnm4DfGSs0TcuN2O4a4GSM54oKM/pFI4/v1EfnSMzSzEj7O3HiBKFQKKHnSjRGWumyZcsSZu4AVVVV+Hw+mpqaGB4eTth5EkFIMilF8uDRNkBPkczI1LtqPHGa5GTg8XhYWaf3+YxSxkBfV1LWpInJ4IUQ+4Czmqa9CqwG/lvTtB+EV9+padqbmqbtBlqFEDtjCzU6zDKriooKMjMz6ezspKenJ6HnSjRmaZaXl0dZWRkjIyO0tLQk9FyJxizNUlJSWLJkiSMztqScPMnpyKlzAGT5LxbpC8n4THKK5LLq8LdKWh1trceT0uBj7aJBCHHflEX3hJc/AjwS6/EXilk3ntfrZfny5ezfv58TJ05MdNk4EbM0M85x9uxZjh8/PtG/7ETM1uzEiRMcP358zi40OxGSk+vQNLQMAlCUoztuKCTxeSE1ji14gFU14Q/ptZxrfZ3ewauouuS13+7GtROdzL7xIs/pVJRm0aM0m5tQCFIieq/aOvVuufJi3X7GA5AV44s+pmPZ4vCH9FrOtp5IyqJjyuDjgFNvvKkozaJHaTY3wdDkFMmuAb2cw5LFeh/5eBzKBE9HYY6HjJRh8KZxprUzKYuOudLg+/v7aW9vJy0tjaqqqoSfz6k3XiSBQGCib3fZsmUJP58bNJNSKoOfB6EpBj8Y0LOpVy3TU4zHApCdAINPT4WiHL12T0vHWFK+vs+VBh9ZMCuRmQ0Gbpi409jYSCAQoLq6mszMBNxtU3CDZp2dnfT19ZGfn09JSUnCz+dUzYKhi3VohoYGCfr1vpPVK8oB3eDzsuN/3hQ/VJbqI7fn+vxIGUq6gVZXGrwxWceMVhVcTPs7fvy4Y981arZmS5Yswev10tDQwOioMytBRWoW7/7j6XBixlYgIJESfD5dnyPHGsGXiTfYR17OxWZ9emr89fN4PNSU6wYf9C+mu7Ml6apKutLgzXxsBigsLKS4uJjBwUHa2tpMOWe8MVuztLQ0amtrCYVCnD592pRzxhuzNXNiobbRKe8JP3yiA4AMX+fFhQlIkTSoLQt/SK+jvcUZmsUTZfBxwmk33lSUZtGjNJubqa/qO9Wkp7IUZA5dXBjnSU6RLDEmoqbXKoN3C+rGix6lWfQozeZmao9lyzl90LO8aPI2iWrBX2ZMsVAteHdgdmaDgdNuvKkozaJHaRY95y/oTl5ToQ/kB4KSjLSLffTxpq4cPJ4QpFbS2uLMrsBYcJ3Bnzt3jgsXLpCfnz9RtdAMnHzjDQ4O0tLSQkpKCjU1NXPvECecrFkwGOTkSb28kjHIbgZO1kxKycBYPgArl+hN+LFxyI7/Ww4nyMn0UJAVAI+X1rPOquETD1xn8JGtKjMyGwycfOMZRrV06VL8/pirV8wbJ2vW1NTE2NgYFRUVZGcnIMdvBiI1c1rG1oXeLoIpeork8roCIP514KeSlgJlhfo13T2Q4tiMrYXiWoNP5MsXpmPZsmV4PB5Onz7N+Pj43DvYCKs0q6qqIj09nY6ODi5ccNY8cqs0KygooKSkhKGhIcdlbDU2noC0KpAhFoX74OP9qr6p6AYftrnUWs6cSa5uGtcZvNn53AYZGRlUV1cTCARoaGgw9dyxYpVmRqE2cF4r3irNIs9pxOAUDh9rAY+fNG8vqeGXaweCkJ2RuCdtv99zcUA3vY5TJ52lWay4zuCN7gYzpttPxTinEYNTUJpFj9IseowUybz0i0VhPEB6WmLPW7Mo/CG9jsbTztIsVlxn8MakGeMdlmZinNNpE3eUZtGjNIue1k49RbIkPzCxzONJXIqkwZKK8Id01UXjaKSUExd9Il/TNxPGOZ124ynNokdpFj3nLuhN9eqyi7OaQhIyEmzwyycmO9Vx5oyzuk9jxVUGf/78efr7+8nLy6OwsHDuHeKM0bJy0ht3hoeHaW1txe/3m1J5cypO1CyyvIIVBu9EzQAujOr35Ipa/W2egaAkPVXvJ08kteWQ4gtBShENjWcTei674SqDj7zpzEyRNHBiy6qxsRGAmpoaU1MkDZyoWXt7O6Ojo5SUlJCTk2P6+Z2o2UB/HwG/Pq30siX5gF7GIJEpkgYZaR5K9FPSct5LMBhM/EltgqsM3mjRWNGqijzv6dOnHZOjbLVm1dXVeL1empubHZOjbGhmRf87QElJCVlZWfT29jqmqmRbawOk64PDVeHKymPjkGeCwUemSgZTqh3/HuBocJXBWznwBZCfn09hYSFDQ0OcPeuMR0GrNUtNTaW6uhopJWfOnLEkhmixsnsG9KqSTuumOXayFXyZ+OkjO1N/uh4dh3wT5oilpzLRgid1kWM0iweuNHirbrzIczvl8VlpFj1Ks+g50ainRuamdk8skxIy0xPflZqaAn94HVyd9mfQ/n3HaBYPXGXwVj86R57bKa0EpVn0KM2i50yHPru7NO9iN5zXAxkJzoEH8Ho9lBbAonJ9DMApmsUDVxm8allFj9IsepRm0XO2V3fyxaUXW+yJLBM8lZxMKCitA5yjWTxwjcGPjIzQ2tqKz+ezJN3PwLjxnNBKsDrdz8BJmoE9WvBOM/i+kXwAllfrne5SSlMmORnkZEJ+sX00a2xs5Gc/+9nEzz/+8Y+59957434e1xh8S0sLUkpqampISUnQ62HmgZNmGXZ0dDAyMkJxcTG5ubmWxeEkzfr7++ns7CQtLY3y8nLL4nBSF00gEGDEo08nXb1cT6EZG4fsDExLZ87NhOIy+2g21eAThWsM3sjAsLIlGnl+O1xEc2F1iqSBk9JLI594vF7rbp+ampqJ9NKxsTHL4pgPbe1tkKb/jesq9SZ7ossETyUz3UN+QSmZWVn09PTMmF46ODjIrbfeyvr167n++ut5/PHHqa2t5f777+eKK65A0zT27NnDTTfdxNKlS/n+978P6E8k9913H2vWrGHt2rU8/vjjsy7/whe+wKuvvsoVV1zBP/3TPwHQ1tbG1q1bWb58OZ///Ofj8nu7zuCtfGwGWLx4MSkpKXR0dDA0NDT3DhZidYqkQUFBAQUFBQwODnLu3DlLY5kLO3RpgZ5eWlVVRSgUoqmpydJY5uLA0U7wZeAPdU9kzZiVImmQlgppKR5qambvptm2bRsVFRXs37+fF154ga1btwL6fI19+/axefNm7rrrLn75y1/y+uuv8+UvfxmAX/3qV+zbt4/9+/fz/PPPc99999He3j7j8q9//ets3ryZffv28Vd/9VcA7Nu3j8cff5wDBw7w+OOP09zcHPPv7RqDNy5yq288n89HbW0tgO3LBtvFrCJjsHs3jR01s/vT4sFTgwDkpJyfWBYIQG7WzN0zHo8nrv9KC7z8yft8VM9h8GvXrmXHjh38zd/8DW+88QZ5eXkAvP/9759Y/+53v5ucnBxKSkpIS0ujt7eXXbt28ZGPfASfz0dZWRnXXnstb7311ozLp+P6668nLy+P9PR0Lr/88rjMC3GNwduliyYyBrvfeHbpoomMwSmaWf3UAw76UmzVu92KcyY/0ZqRIjmVmprZM2lWrFjBnj17WLt2Ld/85jf56le/CkBamh6s1+ud+Gz8HAgEpj1WtEQe1+fzxeW4rjF4owVvhxvPKYOGdumiiYzBKZrZ4UvRKQOtLV36S1crSyJa7HNk0Egp4/7vyVeD1NbNrllbWxuZmZl8/OMf51Of+hR79uyZ1++4efNmHn/8cYLBIJ2dnezcuZN3vetdMy7Pycmhv79/7gPHiPnVpRJA5DR3O9x4TmuNKs3mj2rBR0/3UD6kwtKqiHfzSfNb8NnpzNlFc+DAAe67776JAfRHHnmEO+64Y85jf+ADH+C1115j/fr1eDwevvnNb7Jo0aIZlxcVFeHz+Vi/fj133XUXBQUF8ftFI0nEN+UC/y2YtrY2CciioqJYDhM3nnjiCQnIW265Zc5tW1paTIjoUvr7+yUgU1NTZTAYjHr/eMf9wgsvSEBec801cT3uVGKJOxAISL/fLwE5NDQUx6jmZrq433rrLQnI9evXmxpLNJzvDUnvhiOSzUH53f88L1/aE5LPvxWU//9L0V9zsfLm4aB89bUjEpB1dXVzbm/VvbkAZvRVV7Tg7fTYDM5oWRkDwHV1dZam+xk4QbOWlhYCgQAVFRVkZGRYHc6kpx4ppSUlsufifFc3odQaAC5bordSxwLmVJGcSmEuZBTV4vF4aGpqYnx83NI5M2Zg/Z0dB+zU1QC6aYJuoqFQyOJopsdumi1evBi/309bWxvDw8NWhzMtdtOsoKCAvLw8BgYGOH/+/Nw7WMBb+1vAm4Yv2DkpRdIKg19a6aWiNJ3FixcTDAZtn14aD1xh8HYaLATIycmhtLSU0dFR2trarA5nWuymmd/vt316qd2eFCPLBtv1yWfPYb16ZJbv4vyGsXHIMzEHfipOGZyOB64weLu1rMD+g4ZKs+ix0wCrgd01O9IwAkBR9sDEsmAIsjOs605yQndgvHCFwdutNQr2T/tTmkWP3VrwYH/NGjt8AFQUXyxB4TWxyNh0KIN3GHa88ex+ESnNoseOX4p2b8GfvaD3xSypTJ9YJrFmkpNBMnXRxJxFo2naN4CrgUbgT4QQ4+HlPuCHwHLgbSHEZ2M913QMDg7S0dFBSkoKlZWViTjFgrDzjRcMBidl0dgFO2sG9u7WsuuXYv94KaTAqqWFABPF5JK9Bd/Y2Mju3bv56Ec/CujlgoUQ/Ou//mtczxNTC17TtPVApRBiM3AUiJwR8D6gLbwuS9O0q2I510wYRlVVVYXP50vEKRaEnR+dW1tbGR8fZ9GiRWRlWZDOMAN21syoQJiVlUVpaanV4UxgZ82GhscI+PR3M6xZqZdWHg9CVrr+liWriGzBG184ZuOUcsFXA8+FP28DNs1zXdwwWlXV1dWJOPyCsXNr1I4tUZjcsrJbemlkl5ad8s2Nhk1raysjIyNWhzOJ1/a0gjcFb6CNnKxwmeAxc8sET0dhYSG5ubn09/fT1dU1aZ0qFzyZAuBC+HMfUDjPdXHDuPFqamoScfgFU15eTnp6OufPn+fChQtz72Aidux/ByYq9NkxvdSumvn9fmpqapBS2i699PX9unlmedsnlo0FIC9zpj3MwePxzNgAc1u54Fj74HsB41VAeUD3PNcBoGna3cDdAPfeey9btmyJOoDMzEze8573sHbtWlpbW6PeP5FUVVVx4sQJXn/9dVavXj3tNuPj46bHvW/fPgBKSkoWfO5Exb148WI6Ozt54403EtJSXmjcRtGpsrIyS66z2eJevHgxp0+f5q233rL0zVxTaW44CJ2nqakLsihbf6NTUQZkp8JcEi7+SGLeltXyc/3LpqKign379iGEYPHixRPrS0tL2bZtG5/+9Ke57rrr2LRpE8FgkA0bNtDa2srixYtZs2bNRKPN7/dz+PBhtm3bxtatW+no6ABgw4YNbNu2jd27d0+7PDs7e+I1o6B3AW7cuJGBgQEGBgZYsmQJQoh5zTKfbewxVoPfDfwv4FHgJuB3U9bdAOwMr/uPqTsLIR4GHg7/uKDOsHvuuYd77rmH1tZWWw2ygl569MSJEwwMDMwYmxVxd3Z2AnDFFVcs+NyJinvlypXs3buX/v7+hBx/oXEbj/Lr1q2z5DqbLe5Vq1axc+dO+vr6bHUPZMuDcPQ7XH/zF+gY+DAAHd2S667wsKhori/vxHTRGfqsXr2aZ555ht7e3kmaVVZWsn//fp555hm++93vcvTo0Yl3PBQXF1NUVERhYeHEPikpKZSWlpKdnU1BQcHE8szMTIqKimZcnpubS3p6+sTygoKCScfNysqatN9CicnghRD7NE07q2naq0AT8G1N034ghLgHeBq4PbxurxDitZgidSB2HQCzY7qfgdIseuyu2eKqi92nHuaXQSN3JjaDeybN2traKCws5OMf/zjBYJAnnnhiXsfbvHkzP/jBD/jEJz5Bd3c3O3fu5Fvf+haBQGDa5a2trc4oFyyEuG/KonvCywPAXbEe38nYdaDVroOsoDRbCHbXrKrqYgJEyIIywdMxk2aqXLANywVLac/Snk899ZQE5I033jjjNmbH3dPTIwGZkZEhQ6HQgo+TqLhfeeUVCciNGzcm5PgLiXtsbEx6vV7p8XjkyMhIAqKam9ni3rNnjwTk6tWrTYxodkKhkMzOzpaA3PX6QfnSnpB8QQTlf70YjOm6ixcnT56UgKyqqppxGzt6ygzM6KuumMlqV+zYsrJrup+BHTU7c+YMoVCIqqqqSa9VswuR6aXSorzuqXR2djIwMEBeXj55efmAnkGTnYEtrrvq6mp8Ph8tLS2Mjo5aHU7CUAafQIxZomfOnInbextjxa7pfgYVFRWkpaXR2dlpSh/lfLC7Znl5eRQVFTE8PDyRrWE1E+nLtRc1G7W4imQkKSkpVFdXI6WksbHR6nAShjL4BJKRkUFFRQWBQICWlharwwHsPVgI+kuMI+vp2wG7awb2G2g14qiru6jZmEV14GfCDiULEo0y+ARjty4HOw8WGijNoseumtXUXqx1FAxCTqb13TMGyVB0TBl8grFry0q1RueP3btowH6t0em6aMAeGTQGdtMsESiDTzB2u4iUWUWP+lKMnum6aMDaKpJTUS14RczY6dF5fHycM2fO4PF4Jl6PZ0fsdONJKVUXzQKY6KKpmayZnQzebg2JRKAMPsHYqWXV3NxMMBiksrKS9PT0uXewCDvdeF1dXfT395Obm0thYULq5cUFO2lm1Fjx+/1ULtbLBQeCktQUSPHbpw/ejuml8UYZfIKxU8vKCS1RuJhe2tjYSDAYtDSWyPew2iF/eyYqKytJTU2lo6ODwcFBS2Mxsp9qamrw+/XJ8mPjkGtxFcmp5OfnU1hYyNDQEGfPnrU6nISgDD7BlJaWkpWVRW9vLz09PZbG4oS+ZNALMpWXlzM+Pm55eqkTxiyAiYJYYH166XSajQUg1yY58JHY6cknESiDTzCRtaetvoicYlZgnxvPKV+KYJ/uwOk0Gx2DfBvlwBvYabwnESiDNwG7XESR3Q12x26aOelL0Y6ahSRkZdivi8suDYlEoQzeBOxyEakWfPQozaJnOs28Hntl0BjYRbNEoQzeBOzQsnJKup+BHTSLPL966pk/02kmsdckJwO7aJYolMGbgB36Rru7u7lw4QI5OTkUFxdbFsd8sYNmRrqfz+ejqqrKsjjmix1ao1LKS1rwRgKiasGbjzJ4E7DDRWT3MsFTsYNmZ86cQUpJdXU1KSkplsUxXyKLtIVCiXnl3Vx0dHQwMjJCcXHxxPthpYTMNPB67XfdLV68mJSUFNrb2xkaGrI6nLijDN4Eamtr8Xg8NDU1MTY2ZkkMTupqAP3l1pmZmXR3d9Pb22tJDE7TLDs7m7KyMsbGxix7Af103YBS2qdM8FTslF6aCJTBm0BqaipVVVWEQiGamposicFJg4Vgj/RSp2kG1j/5TKeZlPab5BSJ1ZolEmXwJmH1oKGTBlgN7KKZU1rwYP2g4XSaebBvCx6s1yyRKIM3CasHDZ00YcfALpo58UvRTpp5PJCZZr/+dwOrNUskyuBNwuqLSJlV9CjNomcmg7djBo2B1ZolEmXwJmHlY+Do6CjNzc14vV5qampMP/9CsVKzyHQ/Jz712KqLxuYGb7VmiUQZvElY2UpwWrqfgZWanT17lqGhIQoLC8nLyzP9/AvFSs0GBwc5e/YsqampVFRUTCz3eiDNxgZvZXppKBTiQx/6EPfdd19Czq0M3iSMVsLJkydNrz198uRJwFldDXAxvfTMmTOmp5camjmp9Q5QXl5ORkYG58+fp6+vz9RzGy3guro6fD7fxHKvF1vPvcjJyaG0tJTR0VHT00ubm5v51a9+xU9/+lO83vjbsTJ4kygsLKSoqIjBwUHa29tNPffx48cBuOyyy0w9b6ykpaVRW1tLKBQyvUVqaLZixQpTzxsrHo+H5cuXAxd/B7OYTjOvF/y+mfawD0bMZmt27NixSeePN8rgTcSqi8ipZgVKs4VgJ83ys+1Zg2YqdtIsniiDNxG3XkSJRGkWPXbSzM5dM5HYSbN4ogzeRNx6ESUSpVn0KM2ix2rNEtV9qgzeRKy4iIaGhmhubsbv90/U3HASVmgWDAYnBlmN/mwnYbVZKYOfP6oF7yKsuIgis0GMFyA7CSs0a25uZnR0lPLycnJyckw7b7yI1MysjK2uri66urrIysqivLzclHPGE+Ol6g0NDaZlbI2OjtLY2IjP55tI1Yw3yuBNZNmyZYCeThYIBEw5p5NbVQBVVVWkpaXR3t5Of3+/Ked0umZFRUUUFRUxMDBgWsbWiRMnAF0zp/S7R5Kenk5tbS3BYNC0jC0jZbquro7U1MRMFFAGbyKZmZlUV1cTCARobGw05ZxOTZE08Pl8E90khokkGqdrBuY/+SjNoseMhoQyeJNx40WUaJRm0aM0ix43aqYM3mSMP6YxwSHRJHoihRkozaLHbLNSmkWPMngX4saLKNEozaJHaRY9buzWUgZvMmZeRF1dXXR3d5Odnc2iRYsSfr5EYaZmIyMjnDlzJqGZDWZgpmahUGhifMSJaaUGbvxSVAZvMmZeRJEXkBMzGwzMTPs7depUwjMbzMAwWjMytlpbWxkeHqa0tJT8/PyEniuRVFdXm5ax1dvby7lz58jMzJxUeTPeKIM3mZqaGlJSUmhpaWFwcDCh53LDYzNAcXEx+fn5XLhwgXPnziX0XG7RLCMjYyJjK9Evk3aLZl6v17RCbcbxly9fnpAqkgYxHVnTtG9omvaqpmmPaZqWMmXddZqmNWua9rKmaS/EFqZ78Pv9k0oHJxK33Hgej8e0Jx+3aAbmPS0qzaLHLM0WbPCapq0HKoUQm4GjwB3TbPa4EOI6IcT1Cz2PG3HbRWQGSrPoUZpFj9s0i6UFfzXwXPjzNmDTNNt8KNzC/8sYzuM6jFFzsy4iJ08+MVCaRY/ZZqU0mz9mGXwsxUkKAGMedB9QOGW9AIy/+K81TdslhHg7cgNN0+4G7ga499572bJly4KDGR8fN/1tLAulpKQEgL179/Kxj30sIXGHQqGJiygrKyvu5zBb7+LiYgD2798f03nnivvIkSOA/pYfO11PC9G7sFC/JQ8cOJDQ3+XQoUMA5OXlTXseJ92bBQUFgP47JTJuQ7OCgoKYz1FZWTnjujkNXtO0RcAvpln1HJAb/pwHdEeuFEIMRBzjKWA98PaUbR4GHg7/GFN6RGtr66y/qJ1497vfDUBLSwspKSkJibupqYmRkRHKyspYtWpV3I9vtt4bN24E9N8rlvPOFndPTw9dXV1kZmZSX1+f0MGvaFmI3ps26Q/VZ86cSdjfamxsjObmZjweD1dffTVpaZe+3cNJ96YRf0NDA36/PyFxSyknBr43bdo08UWcCOY0eCFEB3Dd1OWapl0B/C/gUeAm4HdT1ucKIS6Ef7wG+H6MsbqGyJmZiUr7c1O/KFws1Hby5EmCweCkd37Gi8hcbjuZ+0KZmrGVlZUV93OcPn2aUChEXV3dtObuNIqKiigoKKCnp4fOzk4WL14c93O0tbUxODhIcXFxQs0dYuiDF0LsA85qmvYqsBr4bwBN034Q3uROTdPe1DRtN9AqhNgZa7BuoaysjJycHHp7e+np6UnIOdxm8NnZ2VRWVjI+Ps6ZM2cScg63aebz+Sa+GBNVqM1tmkVmbCWqqqSZmsVUIFwIcd80y+4J//8I8Egsx3crxkX09ttvc/r0adauXRv3c7jtxgP9d2ltbeX48eMsWbIk7sd3q2ZHjhzh+PHjXHHFFXE/vls1e+ONN1xh8M5/DnUobmolmEWiMxyUZtHjZs3ccG8qg7cIN11EZqHMKnqUZtFj/C6JmgGsDD4JMHKGE2HwY2NjNDQ04PV6J2bNuoFE5sJLKV1pVomeP+CmHHiDRN6bYK5myuAtwvjjJqLG+bFjx1yV2WBgaHbw4MG4H7upqYnBwUFKSkoSntlgJoZmhw8fJhQKxfXY3d3dtLe3k5GRQVVVVVyPbSXLly+feD/r6OhoXI89PDzMqVOnTGt8KYO3iMsvvxy/38/p06fjXnRs3759AAkZVLOSJUuWkJ2dTXt7e9yLjrlVs9LSUsrLy+nv7497l8P+/fsBWLdunSvSSg0yMzNZsWIFgUCAw4cPx/XYhw4dIhgMsnLlSjIyMuJ67Olwz1/FYaSnp7Nq1SqklHFvkbrVrLxeL+vXrwcumku8cKtmcPF3Mn7HeKE0ix6zNVMGbyFuuYjMRGkWPUqz6HGLZsrgLSQRF5GUUt14C0BpFj1Ks+hRBp9EJOIiamlpobu7m6KiIsfU/4iGRGjW29tLY2Mj6enprsqgMUiEZqOjoxw+fBiPx5OQiXpWE6lZvMqJhEKhia5Fo6sx0SiDtxDjj/zOO+8QDAbjcszIFoKTX9M3E6tXr8bn83H06FGGh4fjckzjplu7di1+f0yTu23J0qVLycrKoqWlhfPnz8flmIcPHyYQCLBixYqE1LixmkWLFlFSUsKFCxdobGyMyzFPnz7NwMAAFRUVlJaWxuWYc6EM3kKKioqoqKhgaGgobm93cvNjM+ivolu5ciWhUChug9Nu18zn87Fu3TogfoPTbtcM9Ew3iN+TjxWaKYO3mNWrVwPOvojMJt5dDkqz6EkGzdxwbyqDtxg3tBLMRplV9CjNokcZvCJm4nkR9fX1cfr0adLS0lw1dXwq8TSrsbExDh06hMfjmejGcCPx1CwUCimDXwDK4JOQeF5E77zzDgBr1qwhJSUl5uPZlcjJTrFOvz9y5Ajj4+MsX76c7OzseIRnS9asWYPX6+XIkSOMjIzEdKzGxkYuXLjAokWLWLRoUZwitB91dXVkZGTQ1NREd3f33DvMQmdnJ62trWRlZZlaH0oZvMVUVVWRm5tLR0cHHR0dMR0rGVpVoL/TtrKyksHBQU6dOhXTsZJFs8zMTC677DKCweDE+0AXSrJoFs/B6cj0SDPLOiiDt5h4Tr9PlhsP4tfloDSLHqVZ9FilmTJ4G+D0i8gKlGbRozSLHqdrpgzeBsTjIhofH5/IC3fzYKFBPDRze1mHqTjdrKzA6Zopg7cB8biIjh49ytjYGEuXLiU3Nzc+gdmYeGjW1NREb28vpaWlrh4sNIjH4HRXVxfNzc1kZmZOvNDbzaxduxaPx8Phw4cXXBt+eHiYo0eP4vV6WbNmTZwjnB1l8DbAqA1/7NixBdeGT6ZWFVysDd/W1rbg2vBuL+swlbKysphrw0fWgPf5fPEMz5ZkZWXFXBve7BrwkSiDtwHxqA2fbAYfj8HpZNMMYn/yUZpFj5WaKYO3CU6+iKxCaRY9SrPocbJmyuBtgvHHF0JEvW8wGGTv3r2AeWVI7UAsmkkpefvttwGlWTQozaLHSs2UwduEzZs3A7B9+/ao60+/9dZb9PT0UFtby+LFixMRni0xNHv++eejLrd85MgRmpubKS4udnVZh6lcc801ALzyyitRz2htbm7m8OHDZGVlceWVVyYiPFty1VVX4fP5eOONN+jt7Y1q3+7ubt58801SUlLYuHFjYgKcBWXwNqG+vp6ysjKam5ujnmn4zDPPAHDrrbcmxWChwYoVK1i6dCnd3d288cYbUe1raHbzzTcnxWChQUVFBVdeeSXDw8O8/PLLUe1raLZlyxbS0tISEJ09ycvL45prriEYDPLcc89Fte/27dsJhUJs3rzZkuw2ZfA2wev1cvPNNwPw29/+Nqp9je1vvfXWuMdlZzwez8TvrDSbP0qz6HGqZsrgbcRCLqL29nb27NlDRkYG1113XYIisy8L0ayvr49du3bh8/m46aabEhWabYnUbL7dgSMjI7zwwgsA3HLLLQmLza4Ymj377LPznkMQDAbZtm3bpP3NRhm8jdiyZQt+v5/du3fT09Mzr32Mx+brr7/e9BxbO3DttdeSmZnJ/v37aWlpmdc+zz33HIFAgE2bNpGfn5/YAG3Ihg0bKC4upqGhgaNHj85rn5dffpmhoSGuvPJKKioqEhyh/Vi1ahW1tbV0dnby1ltvzWufN954g66uLpYuXWrZu36VwduIyL6+7du3z2sfqx8BrSYtLY0bbrgB0FtX8yHZNfP5fGzduhW42ECYC2O7ZGy9g94daPzuC9HMqrExZfA2I5ouh9HRUXbs2AEk740H0WkWCoUmvgiS1eAhOs2klEn/pQjRdwfaQjMppV3+xURLS0ush7CEqXEfPnxYArKoqEgGAoFZ992xY4cE5Nq1axMZ4rTYSe/m5mYJyKysLDkyMjLrtk899ZQEZE1NjQyFQiZFGDvx1ru7u1v6fD7p9/tlb2/vrNseOXJEArK4uHjOa3I67HStRMPUuIeGhmRGRoYEZFtb25z7AjIzM1MODw8nMkwpZ/FV1YK3GStXrqSuro6uri7efPPNWbe1RQvBBixevJj169czODjIK6+8Muu2xkBhsqWUTqWgoICrr76aQCAw8RQ4E8Z1tnXr1qRKKZ1KRkYG733ve4G5uwON7pkbbriB9PT0hMc2E8rgbUZk6t9cfX3J3i8ayXwfn1966SVAaQbz10w1JC7iOM1ma96b/C8m3PIYKKWUzz77rATklVdeOeN+J06ckIAsKCiQ4+PjiQxxWuym9+9+9zsJyGXLls24TXt7uwRkenq6HBwcNDG62EmE3gcOHJCALCsrk8FgcNpt+vr6pN/vl16vV3Z3dy/oPHa7VubLdHE3NjZKQObk5MjR0dFp9xsZGZFZWVkSkM3NzYkOU0rVReMsrr32WjIyMti7dy9tbW3TbmO0EG666Sb8fr+Z4dmSd7/73RQWFnLy5EmOHz8+7TbGY/V73/teMjMzzQzPlqxevZqqqirOnj3Lnj17pt1mx44dBAIBrr76agoKCkyO0H7U1NSwevVq+vv72bVr17Tb7Ny5k8HBQdatW2d56RBl8DYkIyOD66+/HoBHH330kvXj4+P89Kc/BWzwCGgTIlP/fvKTn1yyPhQK8dhjjwFKM4PI7sDpNJNSTlx/SrOLzKZZ5HJbaDZb897kfzHhpsdAKaV88sknJSB9Pp/cvn37xPJQKCT//M//XAKytLRU9vT0mBTpZOyo9/PPPy8B6fF45BNPPDFp3ec//3kJyNzcXNne3m5NgDGQKL1ff/116fP5JCAfffTRSeseeOCBiUyQ06dPL/gcdrxW5sNMcR84cECmpqZKQD700EOT1j344IMSkGlpafLQoUNmhCnlLL66YEOur6/Pq6+vf7O+vn6gvr5+zTTrffX19T+qr69/tb6+/p/nccyYcNtFJKWU999//4QpGRfLP/3TP01cQLt37zYrzEuwq95f+9rXJkzp7bffllJK+cgjj0hA+v1++Ytf/MLiCBdGIvV+6KGHJCBTU1Plq6++KqWU8vHHH5/4svz1r38d0/Hteq3MxWxx/+QnP5logG3btk1KKeVvf/tb6fV6JSB/+tOfmhWmlAky+JT6+vqS+vr6H89g8H9QX1//QPjzD+vr66+a45gx4caLKBgMyjvuuEMCsq6uTv7oRz+SHo9HAvLnP/+5iVFeil31DoVC8pOf/KQEZEVFhXzsscek3++XgHz44YdtG/dcJDruz3zmMxPzL372s5/J9PR0CcjvfOc7MR/brZr/3d/93UQD7Oc//7nMzs6WgPzyl79sToAXib/By4tGPpPBf6O+vv73w58/VF9f/7k5jhUTbr2IBgcH5YYNGyQw8e8rX/mKSdHNjJ31Hh0dlddee+0kzf76r/9aSmnvuGcj0XEHAgF5yy23TNLs7rvvjstkMLdqHgwG5Z133jlJs4985CNWTKCb0Vc9Ukb3compaJr2Y+DbQoiDU5Y/DHxPCLFP07QbgPcKIf52yjZ3A3cD3HvvvfVbtmxZcBzj4+OkpKQseH+rmE/cZ8+e5bbbbqOtrY0PfOADPPjgg5ZP0rG73j09Pbz//e+noaGBG2+8kR/+8If4fD7bxz0TZsQ9MDDA7bffztGjR9m8eTOPPvpoXM7pZs2Hh4e588472bt3L/X19Tz++OOmT2yqrKyc0QzmzK/TNG0R8ItpVn1YCNExy669gFHhPg/onrqBEOJh4OHwjzF907S2tlJZWRnLISxhPnFXVlby+uuv89JLL/FHf/RHtnjZgt31rqys5He/+x3bt2/nzjvvnEiLtHvcM2FW3Dt37uTpp5/mjjvuICcnJy7HdLvmL7zwAk888QQf/OAHbVeddE6DD5v4dQs49m7gBmAncBPwHws4hiJMVVUVf/zHf2x1GI6ivLycu+66y+owHEVJSQmf/OQnrQ7DURQUFPAnf/InVocxLTHlwWua9gxwI/BDTdPuCi/7QXj100C1pmmvAiNCiNdiOZdCoVAooiOmKZBCiEsKeggh7gn/HwDuiuX4CoVCoVg4aiarQqFQuBRl8AqFQuFSlMErFAqFS1EGr1AoFC5FGbxCoVC4lJhnsioUCoXCnqgWvEKhULgUZfAKhULhUpTBKxQKhUtRBq9QKBQuRRm8QqFQuBRl8AqFQuFSlMErFAqFS4mpmqRZaJrmB/4/4IgQ4nWr44kGTdNSgI8Cx51UMtmpmjtc7/8JHAZ2CyEGLQ5pXoT1/nPgBLBLCDFgcUjzxqnXeDTYfqKTpmmlwA+BRvQ3Q20HtgkheqyMaz5omnYt8M/Aa0Ah8AjwohAiZGVcc+FUzR2sdz7wXaAfGAr//x9CiHYr45oLTdM2At9Df7nPOHAE+E8hxJClgc0Dp17j0WLbLhpN04z30g0DrUKIvwS+ASwC7rAssHmiaZoPSAfuEUJ8GvgtcL0QIqRpmrUvVJ0BJ2vuUL2NV1r2A6nAl4Cvhn/+tFVxzYewpmPAp4UQ9wJPAnVCiCG76g3OvsYXgu1a8JqmFQNfA7qAF4C3gb8EfgKcAa5CfwXgvwshGi0Kc1o0TasAvgD8KPyy8QKgVwghNU2rQm+lfVQIMW5poFNwquYO1rsQ+Ef0LtJngW3AnwKHhBDPh3+v/w08JITYb1mgUwjH9W3gX4QQb2ialiuEuBBel4Nu8rfZsQXv1Gs8VuzYgr8D/YXdv0D/A9QBOcAG9HhPAh70Fo9tCD/yfRW4HHgAQAjRI4QwvkE3AmfsZjZhHKe5w/Xeit6l8TXg5vDPKUCtpmlVQog29O6ORdaFOBlN07KBzwA1wN8BGOYe5l3ofdm2M/cwjrvG44GtDF7TNC9QBvxCCLEP+CX6H0AA9eiP3B1AFZBpVZwz0A38byHEDcCopmkfh4lBKNBv4Cc1TfsDTdP+XNO0LKsCjcTBmjtS7zBpwMtCiFPAT9GNfBDIBj4Y3qYuvMxSIrpbhoDvCCE2AWMRehuGmAq8oGna7Zqm/a2maSUWhDstDr7GY8Y2Bq9pmic8GHYB+B/hxb8CqtG/XX8NXBN+0XcK0GFJoDMQfgetEdPXgE9rmpYW0YK8Hf1m/mPgTauyJMKP0sZnx2geGTc4Su/pWuGjwKbw59fR+7LPAk8BxZqmPYluNs1mxDgdRtzGE1H4OjEGICP1Hgsvux190PLDwHYhRKe5EV9E07TFEZ+9TrnGE4ElffDhvtJ/Rh9x3xEeIAsZF5OmaTuBzwshXtc07WPANUKIT4VbC/VWpr9NF7sQIhix3hPuA/4XoEEI8c+apuUBfw0cFEL8l0Vx56P3+6YDLwkhHou4+G2r+XRxT1lvZ72/AqxBN5FfCCHORaz/NfB/w33u7wE+LIS4J3wvXC6EOGDTuKfTexFwKzAshPiZFXFHxHcj+vXyVSHEbzRN84cbA7a9xhOJ6S14TdMy0Pvy6oD7AYQQwfBF4wtv9iDwWU3T1gBFwClN01KFEGMWm/u0sU/ZzHik/RLwVU3T9gGlQoi/t9BsyoCvo7cSv4He+loazjAxujRsp/lMcYfXGTrbUe884D70Pt9PAmvRNY3sQnoIuF3TtPejjyMMaJqWGb4XrDL32eKeTu//o2na20ClEOLfrTb3MD3ACPCHmqZlCyECdr7GE41pLXhN0zSgRQjRoWlasRDivKZp/wHsEUL832lawu9DH9leDvyllTnBC4g9H30gZxXwt0KI0xbG3SSEOKdp2mohxKHw8q+iZ5t8d8r2ttB8AXHnYx+9jeskRwjRH17+EPAO8GMhxGjE9r+HnrlxGfBFIUSrQ+JOB/4CvR/7b4QQDVbEHY6lHmgPD0yjadoGYAVQDqQIIb4WeX/a5Ro3i4QbfLhV/hD6zdcA/E4I8cPwuuXo/aQ3CyG6jMe/yH2naSGbxkJjD7cYii00yMi4G4FXhRCPhNd50Lsv3hZCvGQsi4jdMs2jjTtiPzvp3YA+E/Xh8KP/HwN/BhwCjgM/sYupxBK3pmkFwsJJQVNiPw28IYT4vqZPvrob+CL609+/oWf39EXua6WvmIkZXTSZQLcQ4lr0tLabNU1bGe7/PYGeA/yZ8LalkTva4I+woNiFEOMW38SRcX8FuEXTtJXh2CRQCxzTNC1T07S8yC9VizWPKm5jJ5vp/VVgq6ZpK8MDkDuEEO9G79IrQB9gtQsLjttKcw8TGfv/AW7SNM0w+23oWVZFwA/Q45/ABr5iGmYYvBf4fU3TasKPzjuBO43BPSHE3wN/oWnafmC1CfFEg1NjnzZumMhGWQW8D33CxxVWBTkNbor7jwCEEGfC29wCrMRe9Z+cGjdcGvsrwPuBEuCz6KmQh9Br5NhxLoQpxNXgI1tV4Z9Two9GvwK+HF78I+Da8LctmqZ9CtgFfEQI8WI844kGp8YeZdyXAz70QeKrgP8hhHjFzHgj4nR73L+vadqq8NPG99D72u8TERkpZuLUuMOxzjf2m4DzwLeAzwkh/gb93rRkbMMOxKUPXtPTpH6Anrf7bSFEoxaRghfeZgfwj0KIlzRN+wL6tOynNE3LF0L0xhzEAnFq7AuI+3706dlvAquFEL9TcSc87n3AS0CtEOKoijs6FhD7F4G9QohnwusmjeklI/Ey+A+if3seAPpEOFc5PBDyHfRW7jB6GdcO4Br0b1ZLsh0icWrsC4z7o0KfPWkZSRa3U68Ty+OGBcf+YWFhVo/dWLDBa5p2J/rgxWMiXH9C07StwO+hl93co2nau9FH478khOjWNK0cuBrYKayd6ebI2FXcKm43x+302O1I1Aav6elqn0Uv5nQMCKKns70YFvqDAEKIh6bsN+nRygqcGruK21xU3Obj5NjtTNSDrOE+rRz0b9i/R6+l8ceaPguvHdgLSE3T7tA07RaY6Auz/I/g1NhV3Oai4jYfJ8duZxaaRXMUKNE0LRN4Hn2SxIfD6/YCN6AXJKqEiwWLbIJTY1dxm4uK23ycHLstWajBH0KfCrxC6JMG3kYvBgWwBb1K23UiPOvTZjg1dhW3uai4zcfJsduShU5eOAxsRp891gDkA0a97WeEEL+JQ2yJwqmxq7jNRcVtPk6O3ZYsqAUffjR6FGgD/h29zvI74XWBuEWXAJwau4rbXFTc5uPk2O1KzHnwmqatBo4Le74abVacGruK21xU3Obj5NjthO1euq1QKBSK+GCbV/YpFAqFIr4og1coFAqXogxeoVAoXIoyeIVCoXApyuAVCoXCpdjtLS0KRcIJT4X/PNAohPixpml3Af+B/mKLb1sanEIRR1QLXpGMZKK/Ceiu8M+vAB8BnrIqIIUiEagWvCIZEeH/r9U0TQJngBrgPvSXejcCxcBPgI+jv1jiX4GH0e+ZTwohtmmalgr8I/qXQxawA/i0qkmusAuqBa9IRv42/P8RdHOerlvGqIHyGvqLp/8N/V2fpcDXw+vuB/4aveX/z8DNwPcTErFCsQCUwSuSkefC/58TQvwCGJhmmxDwV8B/h39+TAjxIHqdlLrwsveF/78HvcsnC73qoUJhC1QXjSIZmU99jmEhxJimaUYtlL7w/0HAF7FdAN3og+GfVaNJYRvUxahIRi6gt9CXaZr2MfT+94XwNHoj6RNANbAVvTWvUNgCZfCKpCNcofBb6PXG/5OLre9o+Vr4OJvRB2FvRs/IUShsgaomqVAoFC5FteAVCoXCpSiDVygUCpeiDF6hUChcijJ4hUKhcCnK4BUKhcKlKINXKBQKl6IMXqFQKFyKMniFQqFwKf8P8F9dmjdmYSUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sine_series_st_bin = sine_series.with_static_covariates(\n",
    "    pd.DataFrame(data={\"curve_type\": [1]})\n",
    ")\n",
    "irregular_series_st_bin = irregular_series.with_static_covariates(\n",
    "    pd.DataFrame(data={\"curve_type\": [0]})\n",
    ")\n",
    "\n",
    "train_series = [sine_series_st_bin, irregular_series_st_bin]\n",
    "for series in train_series:\n",
    "    print(series.static_covariates)\n",
    "\n",
    "model = TFTModel(**get_model_params())\n",
    "preds_st_bin = test_case(\n",
    "    model,\n",
    "    train_series,\n",
    "    predict_series=[series[:60] for series in train_series],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbfe3526-d346-4eda-91c3-3e61cc9de0be",
   "metadata": {},
   "source": [
    "That already looks much better! The model was able to identify the curve type/category from the binary static covariate feature."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9298d11f-3864-461d-a364-6b2e5e45d2f6",
   "metadata": {},
   "source": [
    "### 6.4 Forecasting with categorical static covariates\n",
    "The last experiment already showed promising results. So why not only use binary features for categorical data?\n",
    "While it might have worked well for our two time series, if we had more curve types we'd need to one hot encode \n",
    "the feature into a binary variable for each category. With a lot of categories, this lead to a large number of \n",
    "features/predictors and multicollinearity which can lower the model's predictive accuracy.\n",
    "\n",
    "As a last experiment, let's use the curve type as a categorical feature. `TFTModel` learns an embedding for categorical features.\n",
    "Darts' `TorchForecastingModels` (such as `TFTModel`) only support numeric data. Before training we need to transform the `\"curve_type\"` into a integer-valued feature with `StaticCovariatesTransformer` (see section 5.). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "05d6e39c-ff99-4354-96be-756062ab3941",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Static covariates before encoding:\n",
      "static_covariates curve_type\n",
      "component                   \n",
      "smooth                smooth\n",
      "\n",
      "Static covariates after encoding:\n",
      "static_covariates  curve_type\n",
      "component                    \n",
      "smooth                    1.0\n"
     ]
    }
   ],
   "source": [
    "sine_series_st_cat = sine_series.with_static_covariates(\n",
    "    pd.DataFrame(data={\"curve_type\": [\"smooth\"]})\n",
    ")\n",
    "irregular_series_st_cat = irregular_series.with_static_covariates(\n",
    "    pd.DataFrame(data={\"curve_type\": [\"non_smooth\"]})\n",
    ")\n",
    "\n",
    "train_series = [sine_series_st_cat, irregular_series_st_cat]\n",
    "print(\"Static covariates before encoding:\")\n",
    "print(train_series[0].static_covariates)\n",
    "\n",
    "# use StaticCovariatesTransformer to encode categorical static covariates into numeric data\n",
    "scaler = StaticCovariatesTransformer()\n",
    "train_series = scaler.fit_transform(train_series)\n",
    "print(\"\\nStatic covariates after encoding:\")\n",
    "print(train_series[0].static_covariates)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "638b7f2b-203d-44cc-a5b2-ef3b46c0d4e3",
   "metadata": {},
   "source": [
    "No all we need to do is tell `TFTModel` that `\"curve_type\"` is a categorical variable that requires embedding.\n",
    "We can do so with model parameter `categorical_embedding_sizes` which is a dictionary of: {feature name: (number of categories, embedding size)}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dc7eb492-692d-41ec-958d-6bddbdaed937",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8cac6c0f143d45bca19d71ce1afdc34a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3200df5b97714f6081ef94a257fc125a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 4it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_categories = 2  # \"smooth\" and \"non_smooth\"\n",
    "embedding_size = 2  # embed the categorical variable into a numeric vector of size 2\n",
    "categorical_embedding_sizes = {\"curve_type\": (n_categories, embedding_size)}\n",
    "\n",
    "model = TFTModel(\n",
    "    categorical_embedding_sizes=categorical_embedding_sizes, **get_model_params()\n",
    ")\n",
    "preds_st_cat = test_case(\n",
    "    model,\n",
    "    train_series,\n",
    "    predict_series=[series[:60] for series in train_series],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ebcfe69-b5e3-40a6-971d-ddbc48d032a9",
   "metadata": {},
   "source": [
    "Nice, that seems to have worked as well! As a last step, let's look at how the models performed compared to each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dd7784f1-a7b4-4f37-9898-66120a421a97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metric\n",
      "         no st    bin st    cat st\n",
      "RMSE  0.133118  0.027695  0.047532\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metric\n",
      "         no st    bin st    cat st\n",
      "RMSE  0.274442  0.155583  0.131133\n"
     ]
    }
   ],
   "source": [
    "for series, ps_no_st, ps_st_bin, ps_st_cat in zip(\n",
    "    train_series, preds, preds_st_bin, preds_st_cat\n",
    "):\n",
    "    series[-40:].plot(label=\"target\")\n",
    "    ps_no_st.quantile(0.5).plot(label=\"no static covs\")\n",
    "    ps_st_bin.quantile(0.5).plot(label=\"binary static covs\")\n",
    "    ps_st_cat.quantile(0.5).plot(label=\"categorical static covs\")\n",
    "    plt.show()\n",
    "    print(\"Metric\")\n",
    "    print(\n",
    "        pd.DataFrame(\n",
    "            {\n",
    "                name: [rmse(series, ps)]\n",
    "                for name, ps in zip(\n",
    "                    [\"no st\", \"bin st\", \"cat st\"], [ps_no_st, ps_st_bin, ps_st_cat]\n",
    "                )\n",
    "            },\n",
    "            index=[\"RMSE\"],\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b30c424f-42d1-428d-8424-48c2ca052c66",
   "metadata": {},
   "source": [
    "These are great results! Both approaches using static covariates decreased the RMSE by more than halve for both series compared to the baseline!\n",
    "\n",
    "*Note that we only used one static covariate feature, but you can use as many as want including mixtures of data types (numeric and categorical).*"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
